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Speaker 1: Welcome to the Wired to Hunt podcast, your guide to

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Speaker 1: the White Tail Woods, presented by First Light, creating proven

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Speaker 1: versatile hunting apparel for the stand, saddle or blind. First Light,

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Speaker 1: Go Farther, Stay Longer, and now your host, Mark Kenyon.

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Speaker 2: Welcome to the Wired to Hunt podcast. This week on

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Speaker 2: the show, we are discussing trophy hunting, our big game

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Speaker 2: record keeping system and its conservation underpinnings, and how all

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Speaker 2: of this impacts our hunting culture. All right, folks, welcome

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Speaker 2: back to another episode of the Wired to Hunt podcast,

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Speaker 2: brought to you by First Light and it's Camel for

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Speaker 2: Conservation Initiative, And today we are wrapping up our series

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Speaker 2: this month on the culture of our hunting community, and

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Speaker 2: we've got a great one. We've got a couple guests

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Speaker 2: here today that are going to help us explore one

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Speaker 2: of the overarching issues that has hovered over much of

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Speaker 2: this month's conversation, and that's trophy hunting and scoring deer

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Speaker 2: and big bucks and all of that. And these are

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Speaker 2: two folks who come to this issue from the perspective

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Speaker 2: of the record keeping organizations, that being the Boone and

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Speaker 2: Crocket Club and the Pope and Young Club. My guests

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Speaker 2: today are Justin Spring, the executive director at the Pope

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Speaker 2: and Young Club. He also sits on the Records and

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Speaker 2: Ethics committee for the Boone and Crocket Club. And then

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Speaker 2: I'm also joined by doctor John McRoberts. He is a

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Speaker 2: research scientist and wildlife biologist professor at the University of

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Speaker 2: Montana and the program administrator for the Boone and Crockett

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Speaker 2: Wildlife Conservation Program at the University of mind On, Tana.

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Speaker 2: And this whole conversation came together in an interesting roundabout

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Speaker 2: way that I'll describe in more detail once the two

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Speaker 2: of them joined me. But I wrote an article discussing

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Speaker 2: how I have questions about the role that our record

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Speaker 2: keeping systems and organizations play today and if it's time

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Speaker 2: to rethink that a little bit. And this all came from,

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Speaker 2: you know, kind of looking at these very issues that

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Speaker 2: we talked about with Tony and Dan and Andrew about

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Speaker 2: our community is obsession with antlers, obsession with score, all

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Speaker 2: this one upsmanship, and this kind of just obsession over

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Speaker 2: who's the better hunter, Who's killed more big deer, Who's

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Speaker 2: deer is better than this deer? Is this one better?

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Speaker 2: Is this guy better? YadA YadA YadA, and all of

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Speaker 2: this social media chaos that stems from all of that,

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Speaker 2: and it got me to thinking, you know, why are

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Speaker 2: we doing this? How is this helping us? Where did

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Speaker 2: this all come from? Which then sent me down a

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Speaker 2: rabin hole of trying to understand why were these record

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Speaker 2: keeping systems invented in the first place, and what was

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Speaker 2: the original reason for that, what's the historical impetus for this?

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Speaker 2: And is all of this is the record keeping system

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Speaker 2: in this idea of scoring deer and all of these

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Speaker 2: things that have led us to glorifying trophy hunting. Is

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Speaker 2: this achieving what those founders originally wanted for this system?

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Speaker 2: Is this what we were supposed to be doing? Those

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Speaker 2: are the questions that I had, and these questions led

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Speaker 2: me to writing this article, and this article led me

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Speaker 2: to get in touch with Justin Spring, who is the

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Speaker 2: executive director as I mentioned, of the Pope and Young Club.

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Speaker 2: We bumped into each other's show. We start having a

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Speaker 2: great discussion on these very topics which has all led

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Speaker 2: us to where we are today. And the conversation that

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Speaker 2: I'm going to have here with Justin and John in

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Speaker 2: which we explore the history of our record keeping system.

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Speaker 2: How we got to this point. We discuss the conservation impetus,

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Speaker 2: the ways in which our records and scoring deer and

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Speaker 2: other critters was supposed to help with the conservation of wildlife.

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Speaker 2: I'm going to ask these guys some questions about how

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Speaker 2: that might still be going on today. Is it actually

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Speaker 2: going on? Is that a thing? And is it concerning

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Speaker 2: at all that I don't know the answer to that

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Speaker 2: and that you probably don't know the answer to that.

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Speaker 2: If that's why these record keeping systems were created, shouldn't

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Speaker 2: it be important that we're aware of that if it's

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Speaker 2: still going on now. If so, how can we make

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Speaker 2: sure that's more so the case? How can we make

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Speaker 2: this whole thing more useful for conservation? Is there any

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Speaker 2: way to take this whole scoring deer thing, which I

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Speaker 2: think a lot of us believe has gotten out of control?

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Speaker 2: What if we could kind of shift it around to

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Speaker 2: not being a thing that's ranking hunters and who's the

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Speaker 2: best hunter and who can post a picture on Instagram

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Speaker 2: and get more likes? What if this could all lead

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Speaker 2: to better conservation outcomes for deer and elk another wildlife

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Speaker 2: That would be interesting, wouldn't it And how do we

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Speaker 2: think about the implications of what this all means for

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Speaker 2: our hunting culture. That is the set of questions and

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Speaker 2: ideas that I discussed today with Justin and John, and

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Speaker 2: I think it's a really interesting place for us to

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Speaker 2: wrap up this series and leaves me with some hope

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Speaker 2: and intrigue for what the future might look like for

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Speaker 2: these systems and for how we as hunters consider our

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Speaker 2: trophies and how we partake in what might be a

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Speaker 2: grand citizen science experiment if we let it be, and

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Speaker 2: if we participate in that sphere. So that's the plan

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Speaker 2: for today. I really enjoyed this one. I think you're

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Speaker 2: going to learn some new things here and come to

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Speaker 2: think about scoring deer, submitting your deer to record books,

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Speaker 2: all of that I think we'll be thinking about differently,

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Speaker 2: as well as the whole set of ideas around trophy

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Speaker 2: hunting and much more there. So I'm gonna stop rambling

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Speaker 2: and trying to describe this thing and rather just let

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Speaker 2: the conversation happen so you can see what it is

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Speaker 2: for yourself. I enjoy this one. I hope you do too.

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Speaker 2: Here we go, all right, joining me now on the

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Speaker 2: other side of the Internet. I've got Justin Spring and

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Speaker 2: John mc roberts. Thank you, gentlemen for joining me today.

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Speaker 3: Thank you appreciate you having us on.

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Speaker 2: Yeah, I'm excited about this one because I put you

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Speaker 2: guys in the closing well part of the lineup. We've

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Speaker 2: had a full month now discussing various aspects of our

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Speaker 2: hunting culture, and that's a fairly ambiguous thing to consider.

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Speaker 2: There's a lot that goes into that, but I feel

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Speaker 2: like you're roles with the Bonnicrocket Club and Pope and

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Speaker 2: Young put you in touch with a lot of these

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Speaker 2: topics we've been chatting about here, everything from trophy hunting

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Speaker 2: and antler scoring and fair chase ethics, technology, gear use,

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Speaker 2: science and conservation and the intersection of those things with

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Speaker 2: the management of wild game and hunting regulations and all

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Speaker 2: that kind of stuff. This is very much the world

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Speaker 2: we've been exploring in, very much the world I know

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Speaker 2: that both of you exist in. So first and foremost,

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Speaker 2: thanks for making the time to join me on this

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Speaker 2: and to kind of dive into this set of topics

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Speaker 2: that's not always easy to get into. But the original

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Speaker 2: kind of connecting impetus for this, as you both know.

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Speaker 2: Came from an article I wrote, and this article was

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Speaker 2: titled It's time to rethink big game excuse me, big

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Speaker 2: game record keeping, and this whole thing I don't think

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Speaker 2: i've shared with you guys this, but this whole thing

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Speaker 2: came about because I was asked to write about a

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Speaker 2: particular buck story that had come out a few months earlier,

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Speaker 2: and a guy had shot a world class whitetail, and

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Speaker 2: then he had gone and had it scored by this

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Speaker 2: organization and that organization. In this organization, he thought it

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Speaker 2: was going to be the number two typical buck shot

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Speaker 2: with a bow or I can't even remember the specifics now,

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Speaker 2: but he thought it was gonna be a number two

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Speaker 2: buck in certain rankings. And then each different organization had

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Speaker 2: some different reason there nitpicking it apart, and I found

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Speaker 2: myself talking to him and going through all the details,

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Speaker 2: and I got bored with it. I got to think, like,

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Speaker 2: why am I spending all this time? And why is

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Speaker 2: anybody spending all this time nitpicking over of this buck?

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Speaker 2: Certain time is an abnormal time or is it a

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Speaker 2: shared base? Or is it this thing or that thing?

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Speaker 2: Like why is the story about this deer? About whether

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Speaker 2: it's this score or that score. Why isn't the story

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Speaker 2: about just this person's incredible experience, or about the landscape

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Speaker 2: to produce this deer, or about the time he shared

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Speaker 2: with his family afterwards. I personally have just become so

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Speaker 2: sick of this inches above all kind of thing that

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Speaker 2: sometimes we fall into, and I collectively I consider myself

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Speaker 2: a part of that. Sometimes I've been guilty of that too,

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Speaker 2: And so that's what kind of sent me down this

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Speaker 2: weird rabbit hole that the article became. The article became

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Speaker 2: kind of nothing about that guy's specific buck. It became

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Speaker 2: all about you know, what else is there when it

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Speaker 2: comes to this whole scoring thing. What was it originally

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Speaker 2: here for? Why did we develop a scoring system? What

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Speaker 2: was that all about? And are we living up to

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Speaker 2: that original impetus and the the very very cliff notes

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Speaker 2: version of which I'm going to ask for the both

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Speaker 2: of you to chime in with a little bit more detail.

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Speaker 2: But the original kind of starting point for all this

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Speaker 2: came from with the founders of the Boone and Crockett

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Speaker 2: Club looking at this as a conservation project, in that

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Speaker 2: we were trying to recover these species, many many different

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Speaker 2: species across the nation that had come to the cliff's

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Speaker 2: edge of extinction. In many cases, we are now realizing that, hey,

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Speaker 2: we need to recover these species, and how do we

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Speaker 2: keep track of whether or not that's happening, How do

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Speaker 2: we keep track of if we are seeing success, what

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Speaker 2: kinds of how do we track and measure indicators of

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Speaker 2: the fact that maybe we're doing this in some kind

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Speaker 2: of way. So they started these record keeping systems in

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Speaker 2: those early years, and there was this idea of using

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Speaker 2: this data set to support future conservation efforts. Good data

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Speaker 2: leads to good science, leads to good work on the ground,

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Speaker 2: and better population management. I think was where that started.

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Speaker 2: And so I then spent you know, another five hundred

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Speaker 2: eight hundred words asking questions about is that still happening today,

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Speaker 2: is that how we're using this now, or has it

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Speaker 2: just become, you know, a way for hunters to argue

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Speaker 2: it amongst themselves about who's the better deer hunter, who's

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Speaker 2: killed more two hundred inch deer, who's et cetera, et cetera,

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Speaker 2: et cetera. And I feel like that's like a letdown

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Speaker 2: or a a under utilization of the opportunity we have.

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Speaker 2: So I posted that article, went out there into the world,

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Speaker 2: and Justin, at some point you saw it or somebody

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Speaker 2: sent it to you. And then a few weeks later

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Speaker 2: you and I bumped into each other at the Western

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Speaker 2: Hunt ECKSPO and had a great conversation about these things.

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Speaker 2: This is all a very long, rambling setup to why

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Speaker 2: we are all here today, because i'd like to get

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Speaker 2: your perspective that both of you on some of those

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Speaker 2: questions that I had coming out of that little bit

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Speaker 2: of soul searching that I had when I was working

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Speaker 2: on that piece. So the first thing I'm curious about,

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Speaker 2: and maybe Justin, if you want to kick us off here,

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Speaker 2: the first thing I'd love to hear straight from you

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Speaker 2: is a little bit more about the genesis of this

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Speaker 2: whole thing. Yeah, right, How did we get here with

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Speaker 2: this record keeping system, the set of systems that we

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Speaker 2: have here today, and what were those original goals for them?

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Speaker 4: So to start with, you know, the system was redevised

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Speaker 4: in nineteen fifty, but we'll get to that. I'll start

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Speaker 4: from the very beginning. You know, we go back to

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Speaker 4: eighteen ninety three, I believe, was the first Sportsman's Exposition

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Speaker 4: in New York. The Theodore Roosevelt was a judge on

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Speaker 4: you know, looking at at the time, what was the

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Speaker 4: best trophy? Right, Hunting wasn't as common, and so from

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Speaker 4: the very beginning of the organization they were interested in

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Speaker 4: the top specimens. You know, they figured out, like the

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Speaker 4: scoring was very basic, man, that one has a real

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Speaker 4: nice g two five points.

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Speaker 3: Oh, I don't like the color of.

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Speaker 4: This negative too, So they could see right away, like, man,

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Speaker 4: this whole judging of an animal off of somebody's criteria

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Speaker 4: doesn't work. At the same time, they completely felt that

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Speaker 4: all wildlife was going extinct, and they weren't wrong.

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Speaker 3: At the time.

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Speaker 4: Nineteen oh six the club started making or started compiling

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Speaker 4: the National Collection of Heads and Horns, And so even

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Speaker 4: in your article you mentioned the nineteen oh six book

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Speaker 4: that was a field journal that the club kind of

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Speaker 4: put together to for hunter. It wasn't a scoring per se.

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Speaker 4: It was more of a journal of hey, I took

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Speaker 4: this animal. Here's some useful measurements that may be able

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Speaker 4: to be used. So in the beginning they were trying

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Speaker 4: to find the best of every specimen to save for

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Speaker 4: our generation, thinking that no big game was going to

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Speaker 4: be available. So that that's the very beginning of Boone

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Speaker 4: and Krakat. That ties to your trophy hunting to where

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Speaker 4: populations couldn't sustain harvest on young and females. So looking

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Speaker 4: for only the oldest, most mature animal was the way

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Speaker 4: that hunting could continue. You know, if you read articles

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Speaker 4: from back then, people are apologizing to the public if

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Speaker 4: they took a second moose in their lifetime. You know,

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Speaker 4: we'll get into this later, but I think that there's

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Speaker 4: a lot to say that the fact that we can

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Speaker 4: go shoot three or four deer every year is so

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Speaker 4: much different than what these guys were facing.

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Speaker 3: So anyway, that was the beginning.

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Speaker 4: From nineteen oh six to twenty six, I believe we

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Speaker 4: compiled this national collection of heads and horns. They were

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Speaker 4: looking for the best specimen for the future. Well, we

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Speaker 4: get into the twenties and thirties, you start having wildlife

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Speaker 4: conservation take off. Pittman Robertson all the efforts of the

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Speaker 4: organization early on towards conservation, and this collection was in

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Speaker 4: the Bronx Zoo. Well. As wildlife populations recover, people don't

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Speaker 4: want to go see heads hanging in a museum anymore.

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Speaker 4: They can go to Yellowstone. Now they can go to

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Speaker 4: these parks and see these animals. So it kind of

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Speaker 4: fell out of fell out of circulation, and they closed

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Speaker 4: down the exhibit. While the club saw these conservation successes

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Speaker 4: and they're like, man, how can we track that?

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Speaker 3: Like, how do we how do we ensure that you.

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Speaker 4: Know, what we're doing rights recognized and what we're doing

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Speaker 4: not doing correctly is recognized. So this is when this

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Speaker 4: group of guys set down to develop the scoring system

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Speaker 4: that we have today that's now criticized on what is it?

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Speaker 3: Perfect?

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Speaker 4: Is it? This is it that they took every species

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Speaker 4: and they said, what is the typical form? What is

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Speaker 4: the common form of this animal? You know, they took

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Speaker 4: the best science at the time and said, you know,

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Speaker 4: bilateral symmetry, massiveness, those are the traits of the healthiest specimens.

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Speaker 4: What is a typical for example, white tailed deer look like, well,

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Speaker 4: it's got you know, a main beam with with times

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Speaker 4: that come off equally spaced roughly matched off the outside

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Speaker 4: of the beam all the way out. So they they

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Speaker 4: decided what the commoner typical form of each animal was

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Speaker 4: or specimen was, and that's what that score was based

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Speaker 4: around to, you know, go to where we are today.

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Speaker 4: And so even even in the beginning, it kind of changed.

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Speaker 4: I mean to your point of how do we go forward?

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Speaker 4: You know, there's things that we can improve on, for sure,

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Speaker 4: but that's that's how we got this system. Now, that's

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Speaker 4: that's so heavily criticized on deductions and this and that,

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Speaker 4: And there's my long rambling answer to your question.

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Speaker 2: No, that's that's great. So so the I think there's

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Speaker 2: there's something that folks are always curious about you and

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Speaker 2: you mentioned there a little bit, the fact that there's

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Speaker 2: there's a lot of criticism around you know, nets are

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Speaker 2: for fish, right, Folks don't like deductions, Folks don't like

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Speaker 2: the fact that we don't give the dear credit for

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Speaker 2: abnormal points, for non typical points, for all that kind

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Speaker 2: of stuff. So I just want to reiterate something you

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Speaker 2: said and then ask maybe if you can add on

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Speaker 2: if there's anything else I'm missing here That a key

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Speaker 2: point is like the scoring system that was put in

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Speaker 2: place was put in place because it should be documenting

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Speaker 2: and points to the healthiest deer. So the idea of

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Speaker 2: being we're looking for indicators of health, and historically abnormal

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Speaker 2: points or asymmetry was indicative of poor health.

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Speaker 3: Correct some form.

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Speaker 4: Of stressor generally, and John can touch on this a

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Speaker 4: little more. He's looked at the research, but most of

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Speaker 4: what you know would be considered a deduction is caused

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Speaker 4: by some stressor be it you know, pedical damage, bug bites,

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Speaker 4: you know, and who knows what it can be, but

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Speaker 4: you know, at the time that was why they were

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Speaker 4: looking for those symmetrical massiveness trophies and Jaw, you want

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Speaker 4: to touch on that from what you've seen in today's research.

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Speaker 5: Yeah, I will, Justin And so Justin mentioned bilateral symmetry.

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Speaker 5: When we get into atypical handlers, the term that folks

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Speaker 5: might want to look into a bit more is fluctuating asymmetry.

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Speaker 5: And that's what a lot of the research is based around.

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Speaker 5: And that not only in antlers, that's in humans, that's

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Speaker 5: in biomedical research. And so the idea is that's a

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Speaker 5: proxy for environmental or genetic stressors of some kind.

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Speaker 3: Now, those could be.

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Speaker 5: Parasites, injury, disease, density dependent issues, and so I've heard

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Speaker 5: the criticisms too, but when this scoring system was developed,

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Speaker 5: it was separate the typical, the bilateral symmetry from the

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Speaker 5: asymmetric and so that's where those classifications came from. And

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Speaker 5: there's more to the story that we've seen this research

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Speaker 5: done on fluctuating asymmetry. Taking it a step farther, it's

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Speaker 5: what about female choice. Are females really selecting for either

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Speaker 5: bilateral symmetry or selecting against asymmetry? And so there's more

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Speaker 5: to the story. But the important thing is with treating

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Speaker 5: this as a scientific at a set which we should

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Speaker 5: explore more consistency and methods and how you score and

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Speaker 5: how you classify makes this data set a lot more powerful,

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Speaker 5: and so there is some value into keeping the scoring

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Speaker 5: system as it's been.

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Speaker 2: Yeah, So that perfectly segues to the next kind of

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Speaker 2: side of this. I'm curious to learn a little bit

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Speaker 2: more about, as we kind of talked about earlier. I

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Speaker 2: can't remember for this before we started recording or after

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Speaker 2: we started recording, but it seems like within the realm

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Speaker 2: of the general public, when I think about the Boon

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Speaker 2: and Crocket record books, or I think about the Pope

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Speaker 2: and Young record books, I only ever hear those things

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Speaker 2: referenced and brought up. I only ever hear score brought

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Speaker 2: up when it comes to oh, my buck was this big,

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Speaker 2: but your buck was only that or this is a

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Speaker 2: top ten buck, or this is a new state record buck,

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Speaker 2: or this is going to be the new world record buck.

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Speaker 2: Or you know, this guy's killed five two hundred inches,

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Speaker 2: he's better than you, or whatever it is. That's the

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Speaker 2: only time I'm ever hearing scoring and record keeping brought

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Speaker 2: up in general conversation with your average deer hunting body

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Speaker 2: of mine. No, you're but it sounds like, sorry, just one,

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Speaker 2: just one more thing. But from what you were saying,

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Speaker 2: justin it doesn't seem like that was ever brought up

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Speaker 2: by mister Theodore Roosevelt or George bird Grenell or Hornaday

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Speaker 2: or anybody back in the early nineteen hundreds when they were,

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Speaker 2: you know, co founding the Booning Kroker Club and this

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Speaker 2: record keeping system. It was. It was not to confirm

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Speaker 2: who the best hunters were, or who has the most

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Speaker 2: two hundred inch yeer whatever. It was to create a

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Speaker 2: scientific data set to help with conservation efforts. Right, can

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Speaker 2: you can you speak a little bit more to the

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Speaker 2: ways in which the data set has been used or

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Speaker 2: was hoped to be used, because I think that's an

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Speaker 2: important thing for us to understand, is is are these

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Speaker 2: records are is this whole idea of measuring antlers? Is

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Speaker 2: this to tell who's the best dang hunter? Or is

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Speaker 2: this to do something better for the future of wildlife?

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Speaker 4: No? I mean it's interesting you bring it up that

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Speaker 4: the only time you hear about it is the next

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Speaker 4: world record or the biggest this or biggest that. I

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Speaker 4: can honestly say, I didn't go into this line of

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Speaker 4: work and conservation and scoring or anything because it was

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Speaker 4: about ranking hunters. It was about the conservation, you know.

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Speaker 4: And I'd always try to tell we had a marketing

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Speaker 4: team at Boone and Crockett, how do we get folks

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Speaker 4: to check out our page? How do we get them

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Speaker 4: to join? You know what always fell flat the thing

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Speaker 4: that excited me the most, Like this county just put

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Speaker 4: in a deer that just made the book. We've never

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Speaker 4: seen this county put out a one to sixty before

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Speaker 4: it got nothing.

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Speaker 3: You put out.

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Speaker 4: There, Hey, potentially the largest typical killed in Ohio by

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Speaker 4: a female. Millions of likes, right, and so you're not wrong.

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Speaker 4: What you see is what people want to see. I

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Speaker 4: guess as lack of the for lack of a better term,

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Speaker 4: we tried to push it out. We try to tell

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Speaker 4: the conservation story. This is why I love getting on

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Speaker 4: a podcast like this with you know, your listeners and whatnot.

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Speaker 4: Has explained this was not what it was ever, it

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Speaker 4: was never supposed to rank the hunter. You know, one

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Speaker 4: thing I always tell folks, and they owe nets are

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Speaker 4: for fish all that. Man, if we were looking to

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Speaker 4: recognize hunters, what do you use gross score? We're looking

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Speaker 4: to recognize conservations. So that's why that net score, that symmetry,

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Speaker 4: all that comes into play. And so you know, the

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Speaker 4: group that started this, we never wanted somebody having a

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Speaker 4: hunt made or break, you know, made or broken by

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Speaker 4: a number that was attached to an animal. That was

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Speaker 4: never what we wanted. I mean, there's areas in the

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Speaker 4: country that Okay, you're not going to get as big

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Speaker 4: of an elk in this area. That number tells you

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Speaker 4: that it's not that you're less of a hunter, that

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Speaker 4: it's not as good of a trophy. It's you know, biologically,

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Speaker 4: why did the elk in this state not grow as

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Speaker 4: big a G four as the elk in that state?

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Speaker 4: You know.

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Speaker 3: And yeah, we're.

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Speaker 4: As frustrated with it as you are. And we don't

401
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Speaker 4: disagree with the problem. You know, the problems of folks

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Speaker 4: making everything about that final number and not about everything

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Speaker 4: else that goes into it.

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Speaker 2: Yes, so what's what's the alternative of the record keeping

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Speaker 2: system that being? Like? Can you give me some examples

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Speaker 2: of how this data set that we have right now

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Speaker 2: is being used for conservation, because I think the average

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Speaker 2: person maybe is thinking like, I don't see how a

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Speaker 2: bunch of Antler scores helps conservation? Could get could you

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Speaker 2: guys share something?

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Speaker 4: So?

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Speaker 3: I know that the state Idaho was looking at a part.

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Speaker 4: One of their biologists was looking at an area and

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Speaker 4: felt that the deer were getting smaller. They took our

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Speaker 4: data and compared it with some stuff they were doing

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Speaker 4: to see if it matched. Oh, Arkansas, I believe put

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Speaker 4: in an Antler point restriction and they saw some trends

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Speaker 4: in their harvest data that they then got b in

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Speaker 4: C data to see if it matched what they were

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Speaker 4: seeing if we were seeing it at our level as well.

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Speaker 4: There was a Wildlife Monographs monteeth at Al that was

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Speaker 4: done by a group of professors that are associated with

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Speaker 4: the club that looked at over time was Hunter's selection

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Speaker 4: potentially causing big game animals to get smaller. You don't

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Speaker 4: hear about a lot of this in the you know,

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Speaker 4: social media or that world, because it's pretty in depth research, John,

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Speaker 4: do you have other good examples? Those are the ones

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Speaker 4: that come to mind for me.

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Speaker 5: Yeah, there's other peer reviewed literature. And in a university setting,

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Speaker 5: that's that's everyone's currency is doing this research that goes

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Speaker 5: out for review by your peers is flaws or looked

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Speaker 5: for so that that paper won't be published. That's how

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Speaker 5: the system worked. And there's a number of papers in

434
00:24:51,720 --> 00:24:54,960
Speaker 5: premiere wildlife journals like the Journal of Wildlife Management, the

435
00:24:54,960 --> 00:24:59,280
Speaker 5: Wildlife Society bulletin the Journal of Mammalogy that have used

436
00:24:59,280 --> 00:25:03,679
Speaker 5: this data set as a variable to comment on what

437
00:25:03,880 --> 00:25:09,399
Speaker 5: goes into antler productivity, from soil minerals to land use type,

438
00:25:09,440 --> 00:25:13,280
Speaker 5: to edges to habitat connectivity. And so there's a number

439
00:25:13,320 --> 00:25:17,040
Speaker 5: of studies that takes the information that was recorded on

440
00:25:17,040 --> 00:25:21,520
Speaker 5: that scoreesheet, aggregates it across the US and can produce

441
00:25:22,000 --> 00:25:25,520
Speaker 5: the highest quality research. The other way that the scoring

442
00:25:25,560 --> 00:25:30,040
Speaker 5: system is commonly used is if antler measurements or horn

443
00:25:30,119 --> 00:25:36,720
Speaker 5: measurements are used as a covariate of some point in

444
00:25:36,760 --> 00:25:41,639
Speaker 5: someone's analysis. And so this isn't technical and it's just

445
00:25:42,040 --> 00:25:48,159
Speaker 5: instead of taking a measurement, say hind foot length, they

446
00:25:48,200 --> 00:25:54,600
Speaker 5: can use the BNC score to help generate conclusions. And

447
00:25:55,359 --> 00:25:58,560
Speaker 5: so those are some of the top tier i'd call

448
00:25:58,600 --> 00:26:02,680
Speaker 5: them research outputs from this from this data set. About

449
00:26:02,680 --> 00:26:04,760
Speaker 5: this time last year, Justin and I had an email

450
00:26:04,800 --> 00:26:08,280
Speaker 5: from a college senior at the University of Wisconsin who

451
00:26:08,280 --> 00:26:15,000
Speaker 5: wanted to do his senior thesis project on whitetail antler

452
00:26:15,040 --> 00:26:19,800
Speaker 5: scores and relate that to climate to land use type

453
00:26:19,920 --> 00:26:22,640
Speaker 5: land cover. And so this is a study that never

454
00:26:22,680 --> 00:26:25,400
Speaker 5: gets published, but it helps helps this young man graduate.

455
00:26:25,760 --> 00:26:30,080
Speaker 5: Presumably he's a hunter, and so it kind of comes

456
00:26:30,080 --> 00:26:36,960
Speaker 5: full circles of citizens science hunters helping generate management and

457
00:26:37,200 --> 00:26:41,040
Speaker 5: conservation output. And so in the beginning mark you ask

458
00:26:41,119 --> 00:26:43,719
Speaker 5: if the science is still happening, It most definitely is.

459
00:26:43,960 --> 00:26:48,600
Speaker 5: I mean, this is still a nationwide long term data set.

460
00:26:49,600 --> 00:26:52,600
Speaker 5: We just probably need to do a better job advertising that.

461
00:26:52,600 --> 00:26:54,200
Speaker 5: That's how it can be used.

462
00:26:56,119 --> 00:27:03,080
Speaker 2: Yeah, it seems like it's type for opportunity, Like it's

463
00:27:03,119 --> 00:27:08,160
Speaker 2: almost an underutilized resource, or let me rephrase this an

464
00:27:08,160 --> 00:27:12,560
Speaker 2: analog or an alternative community that I've seen do something

465
00:27:12,640 --> 00:27:16,000
Speaker 2: really really neat with a citizens science initiative, Like this

466
00:27:16,320 --> 00:27:19,600
Speaker 2: would be the birding community. Right, there's the birding community,

467
00:27:19,760 --> 00:27:23,760
Speaker 2: and they have the e Bird app in which they

468
00:27:23,800 --> 00:27:28,359
Speaker 2: have burders from all across the world documenting every bird

469
00:27:28,440 --> 00:27:31,080
Speaker 2: they see and where they saw it. And this has

470
00:27:31,160 --> 00:27:35,480
Speaker 2: become something that is an exciting opportunity to participate for

471
00:27:35,560 --> 00:27:38,719
Speaker 2: the burder. And then it also helps lead to a

472
00:27:38,840 --> 00:27:43,440
Speaker 2: data set which is of unbelievable conservation value to researchers

473
00:27:43,840 --> 00:27:47,520
Speaker 2: and managers trying to understand how bird populations are doing

474
00:27:47,560 --> 00:27:50,880
Speaker 2: across the nation, the impacts on the environment, environmental impacts

475
00:27:50,880 --> 00:27:55,600
Speaker 2: on bird populations, et cetera. We have a similar set

476
00:27:55,600 --> 00:27:59,960
Speaker 2: of invested users in hunters who are out there doing

477
00:28:00,000 --> 00:28:03,119
Speaker 2: doing something. And in our case, we're not necessarily documenting

478
00:28:03,119 --> 00:28:07,120
Speaker 2: a sighting. We are able to document a harvest or kill,

479
00:28:07,720 --> 00:28:10,960
Speaker 2: and we have a data point every single time we

480
00:28:11,080 --> 00:28:15,720
Speaker 2: kill an animal. And so I see the Boon and

481
00:28:15,720 --> 00:28:18,320
Speaker 2: Crockett record books, the Pope and Young record books. This

482
00:28:18,440 --> 00:28:22,679
Speaker 2: is like it's it's collecting a micro slice of the

483
00:28:22,800 --> 00:28:26,879
Speaker 2: data out there. And I'm just curious if there is

484
00:28:26,920 --> 00:28:30,880
Speaker 2: an opportunity for more, Like, is there an opportunity for

485
00:28:31,040 --> 00:28:33,919
Speaker 2: there to be greater participation from the hunting public in

486
00:28:33,960 --> 00:28:37,360
Speaker 2: this if it were to be framed as a citizen

487
00:28:37,440 --> 00:28:41,760
Speaker 2: science opportunity and not a hey get yourself in the

488
00:28:41,800 --> 00:28:45,600
Speaker 2: record books so you can, you know, be a big

489
00:28:45,640 --> 00:28:50,160
Speaker 2: bad hunter that killed the Boone and Crocket buck. Is

490
00:28:50,200 --> 00:28:55,280
Speaker 2: there any opportunity for for looking at record keeping in

491
00:28:55,320 --> 00:28:57,760
Speaker 2: the hunting world in that kind of way? Is there

492
00:28:57,800 --> 00:28:59,960
Speaker 2: an argument to be made, a pitch to be made

493
00:29:00,080 --> 00:29:02,080
Speaker 2: to the average hunter out there that's like, hey, man,

494
00:29:02,440 --> 00:29:05,160
Speaker 2: you should submit your buck to Boone and Krockett or

495
00:29:05,160 --> 00:29:07,280
Speaker 2: to Pope and Young, even if you don't care if

496
00:29:07,280 --> 00:29:08,959
Speaker 2: you're in the top fifty for the state or not,

497
00:29:09,040 --> 00:29:10,960
Speaker 2: just because like, hey, this is good for conservation, this

498
00:29:11,040 --> 00:29:14,000
Speaker 2: is good for science. And if that is the case,

499
00:29:15,800 --> 00:29:17,520
Speaker 2: are there any changes we can make to how we

500
00:29:17,600 --> 00:29:19,760
Speaker 2: do this so that that data is even more variable

501
00:29:19,880 --> 00:29:22,719
Speaker 2: or more thorough, John, do you have thoughts on that

502
00:29:22,840 --> 00:29:23,720
Speaker 2: or justin.

503
00:29:23,960 --> 00:29:26,880
Speaker 5: Well, I think it's spot on. I mean, if we

504
00:29:26,920 --> 00:29:30,800
Speaker 5: could expand the scope of this data collection, it'd be

505
00:29:30,840 --> 00:29:34,080
Speaker 5: all the more powerful. Interestingly, you mentioned eBird. I mean

506
00:29:34,800 --> 00:29:37,239
Speaker 5: that's tied with the Internet, but prior to that, it

507
00:29:37,360 --> 00:29:40,280
Speaker 5: was the North American Breeding Bird Survey, which came online

508
00:29:41,240 --> 00:29:43,600
Speaker 5: or came it about in the mid sixties, and so

509
00:29:44,120 --> 00:29:49,040
Speaker 5: interestingly this data set has fifteen years on one of

510
00:29:49,040 --> 00:29:53,080
Speaker 5: the oldest beyond there. I think that it would be

511
00:29:53,200 --> 00:29:59,000
Speaker 5: fantastic to message that exact need to the hunting community.

512
00:29:59,160 --> 00:30:02,960
Speaker 5: I mean, the more data from that we can collect

513
00:30:03,080 --> 00:30:06,520
Speaker 5: with this data set, the more powerful the conclusions will be.

514
00:30:08,160 --> 00:30:10,280
Speaker 2: Yeah, what are you what are your thoughts about? Justin

515
00:30:10,440 --> 00:30:12,480
Speaker 2: we spend a good time amount of time talking about

516
00:30:12,480 --> 00:30:13,040
Speaker 2: this in Utah.

517
00:30:13,080 --> 00:30:15,640
Speaker 3: No, I think that the more data we can collect

518
00:30:15,640 --> 00:30:16,040
Speaker 3: the better.

519
00:30:16,400 --> 00:30:18,120
Speaker 4: When I was when I was at Boone and Crockett,

520
00:30:18,160 --> 00:30:22,120
Speaker 4: we've got many states have a state level organization that's

521
00:30:22,160 --> 00:30:25,400
Speaker 4: collecting data with a lower minimum score. If if we

522
00:30:25,480 --> 00:30:29,280
Speaker 4: can tie that together with you know, Boon and Crockets data,

523
00:30:29,320 --> 00:30:34,720
Speaker 4: that increases our data data set size. The one issue

524
00:30:34,760 --> 00:30:39,040
Speaker 4: that we have, you know, with being too much shittizen

525
00:30:39,120 --> 00:30:41,160
Speaker 4: science is the way that Boone and Crockets set this

526
00:30:41,360 --> 00:30:45,160
Speaker 4: up is that each person that does the scoring is trained.

527
00:30:45,200 --> 00:30:47,600
Speaker 4: It's a five day workshop and so there is a

528
00:30:47,640 --> 00:30:53,520
Speaker 4: standardization of the data that we we can look at

529
00:30:53,520 --> 00:30:55,880
Speaker 4: that data. It's all been reviewed, they've all been trained

530
00:30:55,880 --> 00:30:59,400
Speaker 4: the same way, so we have specific people. That's where

531
00:30:59,440 --> 00:31:01,880
Speaker 4: it gets a little little bit problematic to really blow

532
00:31:01,920 --> 00:31:04,760
Speaker 4: this up to where everything gets scored and taken care of.

533
00:31:04,880 --> 00:31:07,840
Speaker 4: Is having those official measures that that have been taught

534
00:31:07,840 --> 00:31:10,240
Speaker 4: how to do this, the correct methodology and what not

535
00:31:10,400 --> 00:31:10,920
Speaker 4: to get it.

536
00:31:12,040 --> 00:31:12,800
Speaker 3: But we are.

537
00:31:12,720 --> 00:31:15,640
Speaker 4: Always looking for you know, the example I give and

538
00:31:15,680 --> 00:31:18,560
Speaker 4: I hear heard this a lot and still do is that, Well,

539
00:31:18,560 --> 00:31:20,320
Speaker 4: I have one in the book, but it's bigger, So

540
00:31:20,360 --> 00:31:22,760
Speaker 4: I don't want to put this one in when we're

541
00:31:22,800 --> 00:31:25,560
Speaker 4: looking at trends. Man, one smaller than the one that

542
00:31:25,600 --> 00:31:28,000
Speaker 4: you killed before is just as valuable as one bigger.

543
00:31:28,120 --> 00:31:31,840
Speaker 4: You know, we don't only I mean, we're defensive as hunters,

544
00:31:31,880 --> 00:31:34,960
Speaker 4: but man, we don't only want to just show the positive.

545
00:31:35,000 --> 00:31:35,840
Speaker 3: We want everything.

546
00:31:35,880 --> 00:31:37,840
Speaker 4: I mean, how many people have said, oh, there used

547
00:31:37,880 --> 00:31:40,760
Speaker 4: to be you know, dear ten percent bigger in this

548
00:31:40,920 --> 00:31:44,400
Speaker 4: county up until this happened. Well you know these these

549
00:31:44,480 --> 00:31:46,680
Speaker 4: record books, Pope and Young and Boone and Crockett, we're

550
00:31:46,680 --> 00:31:48,520
Speaker 4: the only ones that have the data that can really

551
00:31:48,600 --> 00:31:50,800
Speaker 4: check that and say, is that you know the case

552
00:31:50,960 --> 00:31:53,800
Speaker 4: or did they move? Did they change you know, did

553
00:31:53,800 --> 00:31:56,240
Speaker 4: they change selectivity of where where they're living? Are we

554
00:31:56,280 --> 00:31:58,120
Speaker 4: seeing them now more on edge? Where they used to

555
00:31:58,160 --> 00:32:01,680
Speaker 4: be timber. And so every single data point, if it

556
00:32:01,720 --> 00:32:04,400
Speaker 4: makes any book, be it State or Pope and Young

557
00:32:04,520 --> 00:32:07,000
Speaker 4: or Boon and Crockett or all the above, I think

558
00:32:07,000 --> 00:32:09,000
Speaker 4: it's very important to put them all in and if

559
00:32:09,040 --> 00:32:12,080
Speaker 4: you're fortunate enough to, you know, find yourself in a

560
00:32:12,120 --> 00:32:15,440
Speaker 4: situation to harvest one that meets those minimums. I mean,

561
00:32:15,840 --> 00:32:18,560
Speaker 4: forty bucks, thirty five bucks, whatever the State fee is

562
00:32:18,640 --> 00:32:21,040
Speaker 4: versus poping Young or Boone and Crockett. That's a that's

563
00:32:21,080 --> 00:32:24,600
Speaker 4: an investment in conservation, you know. And I always try

564
00:32:24,600 --> 00:32:27,080
Speaker 4: to tell folks like, don't don't worry if it doesn't

565
00:32:27,080 --> 00:32:29,960
Speaker 4: make all time, don't worry. It's a data point that

566
00:32:30,000 --> 00:32:32,520
Speaker 4: we need to see for us to do our mission,

567
00:32:32,920 --> 00:32:35,120
Speaker 4: you know, and we'd love to have you participate. So

568
00:32:35,240 --> 00:32:38,160
Speaker 4: I think there's a huge call for it, you know.

569
00:32:38,280 --> 00:32:40,880
Speaker 4: It's just we have we can't. We can't throw the

570
00:32:40,960 --> 00:32:42,640
Speaker 4: data out that we've built. We just how do we

571
00:32:42,680 --> 00:32:45,040
Speaker 4: expand on what we've had to make it a more

572
00:32:45,160 --> 00:32:47,280
Speaker 4: robust data set without losing quality?

573
00:32:49,480 --> 00:32:54,600
Speaker 2: So then why not remove the minimum score? Why can't

574
00:32:54,600 --> 00:32:56,280
Speaker 2: I kill a hundred? Why isn't my one hundred and

575
00:32:56,360 --> 00:32:58,280
Speaker 2: ten inch five and a half year old buck that

576
00:32:58,360 --> 00:33:00,920
Speaker 2: I killed off of a fresh cli cut in southern

577
00:33:00,920 --> 00:33:04,360
Speaker 2: Michigan a useful data point for future conservation efforts.

578
00:33:04,400 --> 00:33:08,120
Speaker 4: One hundred percent is it's just the feasibility of maintaining

579
00:33:08,160 --> 00:33:08,719
Speaker 4: all that.

580
00:33:08,720 --> 00:33:09,520
Speaker 3: That's why you know.

581
00:33:09,560 --> 00:33:12,960
Speaker 4: Boone and Crockett said, we can't keep track of every

582
00:33:13,000 --> 00:33:17,200
Speaker 4: animal taken, so how do we extrapolate? Okay, let's let's

583
00:33:17,240 --> 00:33:20,320
Speaker 4: take the very top tier. Right, we know if they've

584
00:33:20,360 --> 00:33:22,680
Speaker 4: reached this minimum score, which is why we don't look

585
00:33:22,680 --> 00:33:24,520
Speaker 4: for the biggest every year, we're just saying, hey, but

586
00:33:24,560 --> 00:33:27,720
Speaker 4: it hits this level. Everything had to be in place

587
00:33:27,720 --> 00:33:31,480
Speaker 4: for it to get there. So in that case, we

588
00:33:31,560 --> 00:33:33,880
Speaker 4: can't say, oh, the problem is this, or the problem

589
00:33:33,920 --> 00:33:38,120
Speaker 4: is that. What we can say is historically we were

590
00:33:38,160 --> 00:33:41,200
Speaker 4: seeing this many meet this threshold. Now we're seeing lesser.

591
00:33:41,280 --> 00:33:45,080
Speaker 4: Now we're seeing more from a single organization. We had

592
00:33:45,080 --> 00:33:47,560
Speaker 4: to limit what we could look at just to maintain

593
00:33:47,920 --> 00:33:50,200
Speaker 4: our integrity and make sure we were doing it correctly.

594
00:33:50,880 --> 00:33:53,440
Speaker 4: So we did not have the ability to take everything

595
00:33:53,480 --> 00:33:55,600
Speaker 4: that was one hundred and five inches or better. But yes,

596
00:33:55,640 --> 00:33:58,440
Speaker 4: it'd be great dat if we could get folks to

597
00:33:58,520 --> 00:34:00,720
Speaker 4: support us to the point where we could drop minimum

598
00:34:00,720 --> 00:34:03,320
Speaker 4: scores and record more. That's that's the ideal world for

599
00:34:03,440 --> 00:34:04,080
Speaker 4: data collection.

600
00:34:06,560 --> 00:34:11,680
Speaker 2: Yeah, I could if if if, if money wasn't an

601
00:34:11,680 --> 00:34:14,680
Speaker 2: object and he had all the support, staff and funds

602
00:34:14,680 --> 00:34:17,960
Speaker 2: to do it. Man, it does. I wish that was

603
00:34:18,000 --> 00:34:21,840
Speaker 2: the case. I wish that we could reframe this as like, hey, like,

604
00:34:21,920 --> 00:34:25,440
Speaker 2: let's all participate in getting the best possible data. That

605
00:34:25,600 --> 00:34:29,759
Speaker 2: is because because because data provides the foundation for all

606
00:34:29,840 --> 00:34:33,800
Speaker 2: management decisions, right, and and if we can as hunters

607
00:34:33,840 --> 00:34:37,759
Speaker 2: can move right right, we hope it does. And and

608
00:34:38,600 --> 00:34:41,200
Speaker 2: what a great opportunity for us as hunters to be

609
00:34:41,239 --> 00:34:46,000
Speaker 2: a part of that. Like I would I would love

610
00:34:46,040 --> 00:34:47,600
Speaker 2: to be a part of that. Would like I have

611
00:34:47,719 --> 00:34:50,560
Speaker 2: zero desire to be on a list that shows how

612
00:34:50,560 --> 00:34:53,920
Speaker 2: I rank with other hunters, but I have tremendous desire

613
00:34:54,239 --> 00:34:58,919
Speaker 2: to contribute to a more positive future for whitetail deer

614
00:34:59,080 --> 00:35:00,879
Speaker 2: or elk or whatever might be. And I think there's

615
00:35:00,920 --> 00:35:03,200
Speaker 2: a lot of hunters out there today that feel that way.

616
00:35:04,120 --> 00:35:06,960
Speaker 2: I kind of feel like the market of hunters who

617
00:35:07,080 --> 00:35:10,400
Speaker 2: want to be in a list and ranked that's saturated.

618
00:35:10,760 --> 00:35:13,920
Speaker 2: Like those folks who are going to participate in that

619
00:35:13,920 --> 00:35:17,239
Speaker 2: that they've kind like they've done it. They're there they

620
00:35:17,280 --> 00:35:18,719
Speaker 2: know how to get their name in there, and they've

621
00:35:18,719 --> 00:35:21,640
Speaker 2: been in their bunch. There's this whole other blue ocean

622
00:35:21,800 --> 00:35:24,759
Speaker 2: of hunters who don't care about that, or who are

623
00:35:24,800 --> 00:35:28,000
Speaker 2: sick of hearing about that, who would love to participate

624
00:35:28,040 --> 00:35:32,520
Speaker 2: though in this side of things. How what what opportunity

625
00:35:32,680 --> 00:35:35,840
Speaker 2: is there for that kind of person to get involved

626
00:35:36,680 --> 00:35:40,680
Speaker 2: given the limitations that you're talking about justin you know, I.

627
00:35:40,640 --> 00:35:43,239
Speaker 4: Mean, the one thing is we heard we hear a

628
00:35:43,320 --> 00:35:45,120
Speaker 4: lot of this or heard a lot of it at

629
00:35:45,160 --> 00:35:48,000
Speaker 4: B and C. We actually change rules where if you

630
00:35:48,080 --> 00:35:50,640
Speaker 4: submit all your information to Boone and Crocket, you still

631
00:35:50,680 --> 00:35:52,560
Speaker 4: don't have to have your name listed in the book.

632
00:35:52,800 --> 00:35:56,359
Speaker 4: You can put your trophy in anonymously. We never had

633
00:35:56,400 --> 00:35:59,920
Speaker 4: anybody do it, and so maybe it wasn't out there enough.

634
00:36:00,160 --> 00:36:05,840
Speaker 4: But man, yeah, people worry a lot about Oh, I

635
00:36:05,840 --> 00:36:07,240
Speaker 4: don't want to give up my hunting spot.

636
00:36:07,280 --> 00:36:08,960
Speaker 3: I don't want to do this. I don't want to

637
00:36:08,960 --> 00:36:09,239
Speaker 3: do that.

638
00:36:09,640 --> 00:36:14,399
Speaker 4: You know, it's really hard for me trying to talk

639
00:36:14,440 --> 00:36:17,400
Speaker 4: to somebody. Oh, I'm a conservationist. You know, put that in.

640
00:36:17,520 --> 00:36:19,640
Speaker 4: You've got fifteen deer that are on the wall that

641
00:36:19,840 --> 00:36:22,160
Speaker 4: over time, you know, you could show a trend you

642
00:36:22,200 --> 00:36:25,520
Speaker 4: could show something, you know, get those scores, get those

643
00:36:25,520 --> 00:36:27,080
Speaker 4: in it. If you want to be more involved in

644
00:36:27,120 --> 00:36:29,560
Speaker 4: the citizen science. Both Pope and Young and Boone and

645
00:36:29,600 --> 00:36:33,799
Speaker 4: Crockett are always looking for official measures. You know, sign

646
00:36:33,920 --> 00:36:37,360
Speaker 4: up to come to a course and learn about the system,

647
00:36:37,440 --> 00:36:39,440
Speaker 4: learn about you know, what we're doing, and then be

648
00:36:39,560 --> 00:36:43,239
Speaker 4: one of the representatives for these organizations out there, you know,

649
00:36:43,360 --> 00:36:46,120
Speaker 4: getting those deer, digging those deer out of garages, talking

650
00:36:46,160 --> 00:36:49,160
Speaker 4: to people, getting the stories. You know, that's how we

651
00:36:49,239 --> 00:36:52,600
Speaker 4: build this out. And you're right, I mean the Boone

652
00:36:52,600 --> 00:36:54,960
Speaker 4: and Crockett went with the classic bait and switch. Hey,

653
00:36:55,040 --> 00:36:57,239
Speaker 4: be part of our cool club. Enter your deer and

654
00:36:57,280 --> 00:36:59,560
Speaker 4: you can be in this book. Well, we were getting

655
00:36:59,600 --> 00:37:01,839
Speaker 4: the data. It was a classic bait and switch. We're

656
00:37:01,960 --> 00:37:03,759
Speaker 4: very good at getting those guys that want to be

657
00:37:03,800 --> 00:37:04,279
Speaker 4: on the list.

658
00:37:04,320 --> 00:37:04,880
Speaker 3: And it's cool.

659
00:37:04,960 --> 00:37:07,719
Speaker 4: I mean, hey, I've got a number six mountain goat

660
00:37:07,760 --> 00:37:09,799
Speaker 4: in Oregon. You know, whatever it may be, that's a

661
00:37:09,840 --> 00:37:12,920
Speaker 4: cool thing. But man, the guys you're talking about that

662
00:37:13,000 --> 00:37:15,479
Speaker 4: don't really care about the list and just care about

663
00:37:15,520 --> 00:37:17,920
Speaker 4: the future. They're just as important and so that you know,

664
00:37:17,960 --> 00:37:20,120
Speaker 4: that's why John and I are here is to talk

665
00:37:20,160 --> 00:37:22,680
Speaker 4: to you and you know kind of plead this case

666
00:37:22,760 --> 00:37:25,759
Speaker 4: of you know, this is why everybody should participate and

667
00:37:25,840 --> 00:37:29,759
Speaker 4: it's not about you know, ranking in this and that

668
00:37:29,400 --> 00:37:30,680
Speaker 4: it's never.

669
00:37:30,520 --> 00:37:35,319
Speaker 2: Been Yeah, I think it's it's a it's it's a

670
00:37:35,360 --> 00:37:38,520
Speaker 2: great reminder because I think most of your average hunters,

671
00:37:38,520 --> 00:37:41,680
Speaker 2: and speaking from like my own world I'm in and

672
00:37:41,719 --> 00:37:43,080
Speaker 2: the people I talk to, I don't think any of

673
00:37:43,120 --> 00:37:45,719
Speaker 2: my friends would ever think about submitting their deer to

674
00:37:45,800 --> 00:37:49,239
Speaker 2: the record books because of what we're talking about. But

675
00:37:49,600 --> 00:37:51,680
Speaker 2: when they hear this and they realize like, oh, hey,

676
00:37:51,719 --> 00:37:53,960
Speaker 2: you know what, actually there there's something more to it

677
00:37:54,320 --> 00:37:57,799
Speaker 2: than just like this ego thing, then I bet you

678
00:37:57,880 --> 00:37:59,600
Speaker 2: there will be a bunch of my friends who would

679
00:37:59,600 --> 00:38:02,480
Speaker 2: be much more or interest in doing it, recognizing like, oh,

680
00:38:02,520 --> 00:38:06,120
Speaker 2: this is something that actually might help I. It's not

681
00:38:06,239 --> 00:38:09,719
Speaker 2: that it doesn't take that much time. I can do that.

682
00:38:09,719 --> 00:38:13,200
Speaker 2: That all said, though, when making this case that the

683
00:38:13,280 --> 00:38:19,160
Speaker 2: record books and having our deer scored contributes positively to

684
00:38:19,320 --> 00:38:23,319
Speaker 2: future conservation efforts, it seems to me that if we're

685
00:38:23,320 --> 00:38:27,000
Speaker 2: trying to accumulate this wealth of data that can be

686
00:38:27,120 --> 00:38:30,520
Speaker 2: used to make better decisions in the future, it seems

687
00:38:30,560 --> 00:38:34,839
Speaker 2: like the data is so thin, like there's so much

688
00:38:34,880 --> 00:38:37,520
Speaker 2: more I wish we could get. And you mentioned this

689
00:38:37,640 --> 00:38:41,760
Speaker 2: consistency of data, John, I had questions in that piece,

690
00:38:41,840 --> 00:38:45,640
Speaker 2: like why why can't we pull a tooth and make

691
00:38:45,680 --> 00:38:47,560
Speaker 2: sure that every submission has a tooth so we can

692
00:38:47,560 --> 00:38:49,880
Speaker 2: get an accurate age on that deer. Why can't we

693
00:38:49,960 --> 00:38:56,080
Speaker 2: have some level of more location fidelity so we can

694
00:38:56,120 --> 00:39:00,800
Speaker 2: tie these these data points back to better location sets

695
00:39:00,800 --> 00:39:05,480
Speaker 2: so that we can have more granular location related correlations

696
00:39:05,520 --> 00:39:08,680
Speaker 2: and trends. Why can't we have something like weight of

697
00:39:08,719 --> 00:39:13,080
Speaker 2: the animal or any other biometric data to make sure

698
00:39:13,160 --> 00:39:15,880
Speaker 2: this is more than just an Antler score thing. It

699
00:39:15,920 --> 00:39:18,760
Speaker 2: gives us a better picture of animal health, which should

700
00:39:18,760 --> 00:39:21,440
Speaker 2: give us a better picture of environmental health, which should

701
00:39:21,440 --> 00:39:36,279
Speaker 2: give us a better data set for conservation decisions. Can

702
00:39:36,320 --> 00:39:39,960
Speaker 2: we not add new data categories to these record books?

703
00:39:42,640 --> 00:39:44,200
Speaker 5: First of all, you hit on the top two that

704
00:39:44,239 --> 00:39:48,600
Speaker 5: I'd love to see, and that's location and age and

705
00:39:49,200 --> 00:39:53,279
Speaker 5: B and C now voluntarily asks for an incisor that

706
00:39:53,360 --> 00:39:57,280
Speaker 5: can be aged. But it seems like that's a tall

707
00:39:57,440 --> 00:40:02,560
Speaker 5: order from some hunters to submit that, and so requiring

708
00:40:02,600 --> 00:40:06,160
Speaker 5: it where it was all or nothing, I think we'd

709
00:40:06,200 --> 00:40:09,480
Speaker 5: end up losing data points that still have great value

710
00:40:09,880 --> 00:40:13,319
Speaker 5: and justin hit on the location thing. So folks just

711
00:40:13,360 --> 00:40:17,399
Speaker 5: don't want to give out their location and I kind

712
00:40:17,400 --> 00:40:19,040
Speaker 5: of get that. I mean, we're coming on to spring

713
00:40:19,080 --> 00:40:21,520
Speaker 5: and I don't think I'd give away my best morale

714
00:40:21,600 --> 00:40:22,800
Speaker 5: location for science.

715
00:40:23,239 --> 00:40:26,000
Speaker 3: And so that's just.

716
00:40:25,960 --> 00:40:29,120
Speaker 5: The reality of what we're in. That said, if more

717
00:40:29,120 --> 00:40:32,200
Speaker 5: people could voluntarily give a lot long and could pull

718
00:40:32,239 --> 00:40:35,799
Speaker 5: a tooth, the power of the conclusions that we could

719
00:40:35,920 --> 00:40:39,880
Speaker 5: draw from this data set would skyrocket, and so it

720
00:40:39,920 --> 00:40:42,120
Speaker 5: would be great. It would be great to have that.

721
00:40:42,160 --> 00:40:45,279
Speaker 5: The only reason why I think it's prohibitive is just

722
00:40:46,440 --> 00:40:49,960
Speaker 5: the likelihood of hunters submitting it right now is not

723
00:40:50,560 --> 00:40:51,360
Speaker 5: entirely high.

724
00:40:52,160 --> 00:40:54,799
Speaker 4: You know, and from both organizations, you know, we've done

725
00:40:54,800 --> 00:40:57,160
Speaker 4: everything we could trying to get an age be and

726
00:40:57,239 --> 00:40:58,959
Speaker 4: C pays for it. I mean, it takes a while.

727
00:40:59,000 --> 00:41:00,600
Speaker 4: We have to get one hundred two I think was

728
00:41:00,640 --> 00:41:03,560
Speaker 4: the old deal for them to get the break. But

729
00:41:03,600 --> 00:41:05,799
Speaker 4: if somebody wanted to submit a tooth, we'd say, hey,

730
00:41:05,840 --> 00:41:07,839
Speaker 4: once we got to one hundred, we'll submit these. We'll

731
00:41:07,840 --> 00:41:13,279
Speaker 4: add that there is certain categories of the data that

732
00:41:13,400 --> 00:41:16,600
Speaker 4: do have tremendous AGE data associated sheep comes to mind

733
00:41:16,640 --> 00:41:19,879
Speaker 4: because they're all checked in and so there's that standardized

734
00:41:19,920 --> 00:41:22,239
Speaker 4: aging of rams that is all held in the data set.

735
00:41:22,320 --> 00:41:27,000
Speaker 4: Now we don't publish that, but that is there. Location data.

736
00:41:27,120 --> 00:41:29,600
Speaker 4: We would always take as much as they would give,

737
00:41:29,640 --> 00:41:33,120
Speaker 4: but we just never published more than county. It varies

738
00:41:33,160 --> 00:41:35,239
Speaker 4: from hunter to hunters. Some guys would send in a

739
00:41:35,280 --> 00:41:38,720
Speaker 4: GPS coordinate, like literally down to the spot it was taken.

740
00:41:39,200 --> 00:41:41,480
Speaker 4: We recorded that. That's in the data set. So if

741
00:41:41,520 --> 00:41:43,600
Speaker 4: somebody requests the data and they want to look at

742
00:41:43,600 --> 00:41:45,160
Speaker 4: location that was there for him.

743
00:41:45,800 --> 00:41:46,840
Speaker 3: We just never made.

744
00:41:46,719 --> 00:41:49,880
Speaker 4: That publicly available, you know, to where the general public

745
00:41:49,920 --> 00:41:52,480
Speaker 4: could see, hey, this guy killed this deer or this

746
00:41:52,560 --> 00:41:58,000
Speaker 4: gal killed this deer at this particular spot. In terms

747
00:41:58,000 --> 00:42:01,080
Speaker 4: of the weight, it would be very useful. But that

748
00:42:01,200 --> 00:42:04,280
Speaker 4: goes back to the in the fifties and the sixties

749
00:42:04,280 --> 00:42:07,040
Speaker 4: when this was developed, and that was the sixty day

750
00:42:07,120 --> 00:42:09,720
Speaker 4: drying period. It was the standardization. You know, some people

751
00:42:09,760 --> 00:42:11,759
Speaker 4: were a month in the mountains and they had to

752
00:42:12,320 --> 00:42:14,239
Speaker 4: you know, it's not fair that somebody could shoot this

753
00:42:14,280 --> 00:42:15,960
Speaker 4: animal and get it scored that day and the other

754
00:42:15,960 --> 00:42:17,719
Speaker 4: guy had to wait a month until he got out.

755
00:42:18,280 --> 00:42:20,640
Speaker 4: So they put in this sixty day drying period to

756
00:42:20,719 --> 00:42:23,799
Speaker 4: kind of standardize it. That would be the issue with

757
00:42:23,880 --> 00:42:26,239
Speaker 4: the weights, as we don't not that there couldn't be

758
00:42:26,280 --> 00:42:28,680
Speaker 4: a system developed, but right now we don't have a

759
00:42:28,760 --> 00:42:33,640
Speaker 4: standardized Here's how Boone and Crockett collects the weights of animals.

760
00:42:34,800 --> 00:42:37,640
Speaker 4: They did dor research at one point on moose racks

761
00:42:37,719 --> 00:42:40,400
Speaker 4: to see if the mass of a moose rack varied

762
00:42:40,680 --> 00:42:43,400
Speaker 4: in different areas. And so for a while there was

763
00:42:43,480 --> 00:42:46,040
Speaker 4: actually a diagram on the score chart that said, Okay,

764
00:42:46,160 --> 00:42:49,560
Speaker 4: was it the whole skull, was it cut narrowly, was

765
00:42:49,600 --> 00:42:52,400
Speaker 4: it cut in the middle, And so it kind of

766
00:42:52,440 --> 00:42:55,240
Speaker 4: shows you some of that variation you'd get and trying

767
00:42:55,280 --> 00:42:58,720
Speaker 4: to add a data data point, well how much skull

768
00:42:58,840 --> 00:43:02,160
Speaker 4: is cut in the middle AND's opinion versus mine. And

769
00:43:02,200 --> 00:43:04,239
Speaker 4: so that's why some of those factors that would be

770
00:43:04,280 --> 00:43:07,920
Speaker 4: super awesome just over time, we never had the ability

771
00:43:07,920 --> 00:43:11,080
Speaker 4: to implement, you know, due to variation that kind of

772
00:43:11,239 --> 00:43:13,920
Speaker 4: shot holes in the validity of the measurement.

773
00:43:16,440 --> 00:43:18,920
Speaker 5: And with the way thing when we're let's say we're

774
00:43:18,960 --> 00:43:21,920
Speaker 5: out collaring deer or elk or handle over or whatever,

775
00:43:22,440 --> 00:43:25,399
Speaker 5: weight is it a common variable that won't collect. I mean,

776
00:43:25,440 --> 00:43:28,560
Speaker 5: it has some use but having h class data that

777
00:43:28,600 --> 00:43:32,560
Speaker 5: can take you a lot farther with with management applications.

778
00:43:34,160 --> 00:43:37,400
Speaker 2: So John, from you know, being so involved in the

779
00:43:37,400 --> 00:43:41,680
Speaker 2: science side of things, if you had an unlimited budget,

780
00:43:42,040 --> 00:43:44,160
Speaker 2: if if we guarantee the B and C or P

781
00:43:44,280 --> 00:43:47,280
Speaker 2: and Y had an unlimited budget and all the staffing

782
00:43:47,280 --> 00:43:50,960
Speaker 2: they could ever ask for, and you were tasked with

783
00:43:51,560 --> 00:43:55,239
Speaker 2: renovating the record keeping system so that this could be

784
00:43:55,280 --> 00:44:00,520
Speaker 2: the absolute best tool for future conservation needs in a

785
00:44:00,600 --> 00:44:03,239
Speaker 2: time when we are probably going to have greater conservation

786
00:44:03,360 --> 00:44:06,320
Speaker 2: needs than ever, as the human footprint on the world

787
00:44:06,360 --> 00:44:10,399
Speaker 2: continues to expand, as so many different factors change environmentally

788
00:44:10,440 --> 00:44:12,759
Speaker 2: over the next fifty tow one hundred years. I think

789
00:44:12,800 --> 00:44:16,000
Speaker 2: it is not an exaggeration at all to say that

790
00:44:16,080 --> 00:44:18,719
Speaker 2: we are going to be faced with all sorts of

791
00:44:18,800 --> 00:44:24,920
Speaker 2: difficult choices and unforeseen circumstances in coming decades, right for wildlife.

792
00:44:24,960 --> 00:44:28,239
Speaker 2: So we need the best tools we possibly can have

793
00:44:28,320 --> 00:44:31,759
Speaker 2: heading into that future. So if you were tasked with

794
00:44:32,880 --> 00:44:36,400
Speaker 2: creating this two point zero system, that would be that

795
00:44:36,520 --> 00:44:41,040
Speaker 2: tool for the future. Again, budget staffing, none of it's

796
00:44:41,080 --> 00:44:44,000
Speaker 2: of a concernant. Please tell me what that perfect two

797
00:44:44,000 --> 00:44:45,160
Speaker 2: point zero system would look like.

798
00:44:45,400 --> 00:44:48,279
Speaker 5: Well, I wouldn't do much to change the measuring of

799
00:44:48,280 --> 00:44:52,800
Speaker 5: antlers and horns because I think that's pretty well covered.

800
00:44:53,320 --> 00:44:58,160
Speaker 5: I would have an ultrasound and collect rump fat dead animal.

801
00:44:58,239 --> 00:45:01,920
Speaker 5: I'd collect bone, marrow condition and quantity, and the upper

802
00:45:01,960 --> 00:45:05,480
Speaker 5: femur be good to pull a lymph node to get

803
00:45:05,480 --> 00:45:09,479
Speaker 5: a clearer idea of CWD distribution. Of course, get the tooth,

804
00:45:09,600 --> 00:45:14,840
Speaker 5: get the location, and those are the first couple that

805
00:45:14,880 --> 00:45:19,000
Speaker 5: come to mind. And with that information on every potential

806
00:45:19,040 --> 00:45:22,879
Speaker 5: animal that's harvest you'd have a out of those world

807
00:45:23,000 --> 00:45:25,480
Speaker 5: data set. But that take an unlimited budget.

808
00:45:26,840 --> 00:45:30,080
Speaker 2: Yeah, and so you would, you would you would ask

809
00:45:30,239 --> 00:45:34,640
Speaker 2: for the every animal harvested, no minimum score. Is that correct?

810
00:45:35,440 --> 00:45:37,960
Speaker 5: If you could get it, every animal that was harvested,

811
00:45:38,000 --> 00:45:41,640
Speaker 5: that was roadkilled, that was anything, anything that someone could

812
00:45:41,680 --> 00:45:44,719
Speaker 5: get their hand on, and yeah, that'd be that'd be

813
00:45:44,760 --> 00:45:45,640
Speaker 5: the dream data set.

814
00:45:46,480 --> 00:45:51,520
Speaker 2: Yeah. Okay. Now let's say that your boss came in

815
00:45:51,600 --> 00:45:57,080
Speaker 2: and established a budget and the real world is back

816
00:45:57,120 --> 00:46:00,959
Speaker 2: in play. You have you have the the real set

817
00:46:01,000 --> 00:46:05,239
Speaker 2: of limitations we have in this real world. What is

818
00:46:06,040 --> 00:46:09,560
Speaker 2: what are two changes that you would make if you

819
00:46:09,760 --> 00:46:14,960
Speaker 2: had the ability to make two changes to our system

820
00:46:15,280 --> 00:46:19,879
Speaker 2: or or the data we're trying to collect. What would

821
00:46:19,880 --> 00:46:23,239
Speaker 2: those two changes or asks be within the set of

822
00:46:23,280 --> 00:46:24,160
Speaker 2: limitations that we know.

823
00:46:24,400 --> 00:46:25,560
Speaker 3: Well, something that would be.

824
00:46:27,360 --> 00:46:33,320
Speaker 5: That would be very feasible, and it require looping in

825
00:46:33,400 --> 00:46:39,040
Speaker 5: some statistician and some population modelers. But the amount of effort,

826
00:46:39,320 --> 00:46:42,600
Speaker 5: number of days, number of hours that somebody was hunting

827
00:46:42,600 --> 00:46:47,920
Speaker 5: for a particular animal. And this it's easy to think

828
00:46:47,960 --> 00:46:50,040
Speaker 5: of this on a micro scale where you know where

829
00:46:50,040 --> 00:46:53,160
Speaker 5: this big white tail is on the back wood lot,

830
00:46:53,280 --> 00:46:55,839
Speaker 5: but how much effort went into that because knowing those

831
00:46:56,200 --> 00:47:02,520
Speaker 5: hunter effort metrics, the results and harvest can really help

832
00:47:03,000 --> 00:47:06,560
Speaker 5: with population forecasting. And then again, I know I'm sounding

833
00:47:06,680 --> 00:47:09,799
Speaker 5: like a broken record here, but age goes a long

834
00:47:09,920 --> 00:47:12,160
Speaker 5: long way. Knowing what the age class is for that

835
00:47:12,200 --> 00:47:17,600
Speaker 5: population would be valuable. Most of the animals that would

836
00:47:17,600 --> 00:47:20,759
Speaker 5: make the B and C book that would come back

837
00:47:20,760 --> 00:47:22,440
Speaker 5: to us our older animals.

838
00:47:22,520 --> 00:47:22,879
Speaker 3: And so.

839
00:47:24,320 --> 00:47:27,040
Speaker 5: Having there's ways where you can get proxies for a

840
00:47:27,800 --> 00:47:30,279
Speaker 5: year and a half versus two and a half, that's

841
00:47:30,320 --> 00:47:34,000
Speaker 5: really not applicable here. And so pulling that tooth would

842
00:47:34,000 --> 00:47:37,319
Speaker 5: be the most reliable way and if you had that,

843
00:47:37,360 --> 00:47:38,640
Speaker 5: you'd be a very good shape.

844
00:47:39,840 --> 00:47:44,480
Speaker 2: Yeah, So justin being in the leadership role that you

845
00:47:44,520 --> 00:47:47,319
Speaker 2: are there with P and Y and understanding your own

846
00:47:47,320 --> 00:47:50,200
Speaker 2: limitations and having your previous experience with B and C.

847
00:47:52,600 --> 00:47:56,000
Speaker 2: If you could snap your fingers now and either take

848
00:47:56,040 --> 00:47:58,960
Speaker 2: one of these suggestions that John had or make a

849
00:47:59,040 --> 00:48:01,719
Speaker 2: change of your own desire. What would you like to

850
00:48:01,760 --> 00:48:06,640
Speaker 2: see change or be updated or renovated in some kind

851
00:48:06,680 --> 00:48:11,000
Speaker 2: of way to improve this citizen science opportunity.

852
00:48:11,320 --> 00:48:14,920
Speaker 4: You know, just just participation, I mean, that is a

853
00:48:15,000 --> 00:48:18,880
Speaker 4: leadership role. I mean anything's probably if all of a

854
00:48:18,920 --> 00:48:21,600
Speaker 4: sudden we started getting one hundred percent of the animals

855
00:48:21,600 --> 00:48:23,799
Speaker 4: that were taken that made Boone and Crockett and Pope

856
00:48:23,840 --> 00:48:27,680
Speaker 4: and young, if everybody was participating at that level, we'd

857
00:48:27,680 --> 00:48:30,399
Speaker 4: have the funding to go bigger, you know if if

858
00:48:30,719 --> 00:48:33,799
Speaker 4: age again. Age has been a reoccurring theme. I spent

859
00:48:33,880 --> 00:48:36,200
Speaker 4: fifteen years at B and C trying to figure out

860
00:48:36,200 --> 00:48:39,319
Speaker 4: how to up that age data. I mean I got

861
00:48:39,320 --> 00:48:40,719
Speaker 4: to play with the data. It was a lot of

862
00:48:40,719 --> 00:48:44,880
Speaker 4: fun because we'd have teeth that had replacement and wear ages.

863
00:48:44,960 --> 00:48:49,719
Speaker 3: We had bile tagged animals so known age, and then

864
00:48:49,760 --> 00:48:50,760
Speaker 3: we also had.

865
00:48:52,239 --> 00:48:55,600
Speaker 4: The section tooth data and I could compare them, you know,

866
00:48:55,719 --> 00:48:57,600
Speaker 4: and I can tell you a lot of a lot

867
00:48:57,640 --> 00:48:59,800
Speaker 4: of whitetail hunters want to call their boon and crocket

868
00:48:59,800 --> 00:49:02,280
Speaker 4: dear four and a half, and most of them aren't.

869
00:49:03,360 --> 00:49:04,480
Speaker 3: You know that that.

870
00:49:04,360 --> 00:49:07,520
Speaker 4: Kind of shows some of the some of the hiccups

871
00:49:07,520 --> 00:49:10,239
Speaker 4: of going too much shittis in science. So you know,

872
00:49:10,360 --> 00:49:13,279
Speaker 4: like I said, I to John's point, Yeah, I'd love

873
00:49:13,320 --> 00:49:15,719
Speaker 4: to add a couple of traits. Honestly, there's probably a

874
00:49:15,719 --> 00:49:19,440
Speaker 4: couple of small changes I'd make on the scoring, just

875
00:49:19,440 --> 00:49:21,879
Speaker 4: just because having done it for so long, being like, man,

876
00:49:21,920 --> 00:49:23,960
Speaker 4: I don't understand why they made that rule the way

877
00:49:24,000 --> 00:49:24,359
Speaker 4: they did.

878
00:49:24,880 --> 00:49:28,120
Speaker 3: But again, they've been doing it for, you know, eighty years.

879
00:49:28,160 --> 00:49:30,600
Speaker 4: You can't really throw out eighty years of data just

880
00:49:30,640 --> 00:49:32,319
Speaker 4: because there's a better way to take a gu one

881
00:49:32,400 --> 00:49:32,799
Speaker 4: on a bull.

882
00:49:32,880 --> 00:49:40,000
Speaker 2: Help. Yeah, okay, so I think a reasonable ask for

883
00:49:40,040 --> 00:49:44,960
Speaker 2: the audience. Then it sounds like would be participate a

884
00:49:45,000 --> 00:49:47,200
Speaker 2: little bit more on this if you previously thought this

885
00:49:47,320 --> 00:49:49,920
Speaker 2: was not for you because you don't care about how

886
00:49:49,920 --> 00:49:51,839
Speaker 2: you're ranked within the list of other hunters who killed

887
00:49:51,840 --> 00:49:54,919
Speaker 2: one hundred and seventy inchbuck or whatever it is. There's

888
00:49:54,920 --> 00:49:57,600
Speaker 2: a science, there's a conservation reason to do this. There's

889
00:49:57,800 --> 00:50:01,440
Speaker 2: value in submitting the thing and submitting it as thorough

890
00:50:01,600 --> 00:50:05,640
Speaker 2: of a submission as you possibly can with all the

891
00:50:05,719 --> 00:50:10,399
Speaker 2: data you're willing, because more data the better. And let's

892
00:50:10,400 --> 00:50:14,840
Speaker 2: all do the age. Let's throw in the voluntary age opportunity.

893
00:50:14,880 --> 00:50:16,880
Speaker 2: Let's check that box because it sounds like that's going

894
00:50:16,960 --> 00:50:19,839
Speaker 2: to be very valuable data. And then if we all

895
00:50:19,880 --> 00:50:22,000
Speaker 2: do this, if we all do a really good job

896
00:50:22,040 --> 00:50:25,640
Speaker 2: of participating more, then Justin's going to have the funds

897
00:50:25,920 --> 00:50:29,480
Speaker 2: to be able to take more of these submissions. And

898
00:50:29,640 --> 00:50:31,680
Speaker 2: maybe ten years from now we'll have a case to

899
00:50:31,719 --> 00:50:36,520
Speaker 2: be made to lower the minimum scoring threshold so we

900
00:50:36,560 --> 00:50:40,920
Speaker 2: can have more folks participate or add on one of

901
00:50:40,920 --> 00:50:44,239
Speaker 2: these other asks that John made. So this is a

902
00:50:44,239 --> 00:50:45,920
Speaker 2: step in the right direction, maybe right.

903
00:50:46,600 --> 00:50:47,040
Speaker 3: That's right.

904
00:50:47,120 --> 00:50:50,680
Speaker 5: And the other thing, I mean, participation is key. I've

905
00:50:50,680 --> 00:50:52,360
Speaker 5: deer hunted most of my life. I grew up in

906
00:50:52,400 --> 00:50:55,480
Speaker 5: Missouri around big deer. I've never killed a deer that

907
00:50:56,080 --> 00:50:59,960
Speaker 5: approached one sixty. I know people who do every seas

908
00:51:00,160 --> 00:51:06,160
Speaker 5: and I hound by friends and encourage strangers to submit

909
00:51:06,360 --> 00:51:09,239
Speaker 5: because there is value in this. And so even if

910
00:51:09,239 --> 00:51:14,440
Speaker 5: you're not the one that made the harvest, communicate to

911
00:51:14,480 --> 00:51:17,800
Speaker 5: people who do how much this can benefit conservation.

912
00:51:18,920 --> 00:51:19,680
Speaker 3: By hunters.

913
00:51:19,800 --> 00:51:21,839
Speaker 5: I mean, it's kind of that positive feedback like we'd

914
00:51:21,840 --> 00:51:24,840
Speaker 5: see in PR dollars and buy in dusting. This is

915
00:51:24,880 --> 00:51:27,440
Speaker 5: a way for hunters to step up and benefit conservation.

916
00:51:29,040 --> 00:51:34,239
Speaker 2: Yeah. So I also want to pick both of your

917
00:51:34,280 --> 00:51:39,319
Speaker 2: brains about the flip side of this conversation, which kind

918
00:51:39,360 --> 00:51:42,720
Speaker 2: of ties back to the original thing that my article

919
00:51:42,800 --> 00:51:47,880
Speaker 2: started with, which was the obsession with antlers and antler

920
00:51:47,920 --> 00:51:50,719
Speaker 2: score that many have within their community and that I

921
00:51:50,760 --> 00:51:53,920
Speaker 2: have been guilty of at times too. And there's nothing

922
00:51:53,960 --> 00:51:56,640
Speaker 2: wrong with being excited about big deer. I love big deer,

923
00:51:56,680 --> 00:51:59,440
Speaker 2: I love big elk. They're fascinating, amazing critters, and I

924
00:51:59,480 --> 00:52:04,400
Speaker 2: get a charge out chasing them. But there are definitely

925
00:52:05,080 --> 00:52:07,960
Speaker 2: some downsides to what this has done to our community

926
00:52:08,040 --> 00:52:11,160
Speaker 2: and culture as well, not to mention the fact that

927
00:52:11,640 --> 00:52:16,160
Speaker 2: survey after survey after survey shows that the non hunting public,

928
00:52:16,239 --> 00:52:19,320
Speaker 2: those that are the majority that have the voting power

929
00:52:19,360 --> 00:52:24,360
Speaker 2: to determine our fate, are very strongly telling us for

930
00:52:24,400 --> 00:52:27,360
Speaker 2: decades now that they are not supportive of trophy hunting.

931
00:52:28,560 --> 00:52:31,960
Speaker 2: So there seem to be some risks that come alongside

932
00:52:32,040 --> 00:52:37,279
Speaker 2: of the continued promotion of antler score antler size, that

933
00:52:37,360 --> 00:52:41,680
Speaker 2: kind of thing justin with your role at a company

934
00:52:41,800 --> 00:52:44,680
Speaker 2: or at an organization. You know that has been a

935
00:52:44,680 --> 00:52:47,960
Speaker 2: part of this record keeping for decades. Now, how do

936
00:52:48,160 --> 00:52:53,879
Speaker 2: how do you personally think about this? Do you have

937
00:52:53,960 --> 00:52:57,840
Speaker 2: any worries about are infatuation with antlers?

938
00:52:57,880 --> 00:52:58,279
Speaker 4: Do not?

939
00:52:58,400 --> 00:53:00,560
Speaker 2: Do you see negatives or concerns or it? Are you

940
00:53:00,640 --> 00:53:03,960
Speaker 2: not worried about it from a cultural perspective, from the

941
00:53:04,000 --> 00:53:06,440
Speaker 2: impact of what this says about us as hunters or

942
00:53:06,480 --> 00:53:09,920
Speaker 2: where hunting's going. Where are you personally at with all that?

943
00:53:10,880 --> 00:53:16,040
Speaker 4: You know, Obviously, the majority of my job the promotion

944
00:53:16,200 --> 00:53:19,440
Speaker 4: of a lifestyle, a promotion of archery, a promotion of

945
00:53:19,480 --> 00:53:22,760
Speaker 4: records keeping, of the conservation model that's been around forever.

946
00:53:23,560 --> 00:53:25,160
Speaker 3: You know, the success of my job is.

947
00:53:25,120 --> 00:53:27,759
Speaker 4: Dictated by having the buy in of that seventy two

948
00:53:27,840 --> 00:53:30,400
Speaker 4: percent of the country that doesn't hunt themselves but approves

949
00:53:30,400 --> 00:53:33,920
Speaker 4: of it. And so if we're doing something that that

950
00:53:34,080 --> 00:53:37,120
Speaker 4: seventy two percent finds offensive, there's two.

951
00:53:37,040 --> 00:53:38,960
Speaker 3: Ways to deal with that. Either we come together as

952
00:53:39,000 --> 00:53:39,520
Speaker 3: a group and.

953
00:53:39,440 --> 00:53:41,279
Speaker 4: Say, hey, we just got to knock it off because

954
00:53:41,280 --> 00:53:44,080
Speaker 4: they don't like it, or we have to say, hey,

955
00:53:44,760 --> 00:53:46,400
Speaker 4: we've got to fix our act here a little bit

956
00:53:46,400 --> 00:53:49,640
Speaker 4: and explain to them why pursuing that oldest, biggest, most

957
00:53:49,640 --> 00:53:53,520
Speaker 4: mature animal in some situations is what you should do.

958
00:53:53,640 --> 00:53:58,480
Speaker 4: In other situations when we're trying to lower the density

959
00:53:58,520 --> 00:54:01,000
Speaker 4: of deer, maybe maybe we should be shooting those bucks

960
00:54:01,040 --> 00:54:02,880
Speaker 4: and putting it all that effort. Maybe we should be

961
00:54:02,960 --> 00:54:07,040
Speaker 4: killing five or six dos. And so I do think

962
00:54:07,120 --> 00:54:11,480
Speaker 4: that this over this infatuation, this do anything for the

963
00:54:11,480 --> 00:54:14,560
Speaker 4: big deer is problematic. And we have examples how many

964
00:54:15,120 --> 00:54:18,919
Speaker 4: how many you know, creators, TV show personalities, different things

965
00:54:18,960 --> 00:54:23,000
Speaker 4: along those lines have pushed it so far to be

966
00:54:23,160 --> 00:54:25,520
Speaker 4: that guy and to be the hero. Well, we as

967
00:54:25,600 --> 00:54:29,080
Speaker 4: hunters are still watching them too, And so you know

968
00:54:29,719 --> 00:54:33,560
Speaker 4: how we separate this out in terms of I honestly

969
00:54:33,600 --> 00:54:36,719
Speaker 4: see absolutely nothing wrong with antler scoring. I see absolutely

970
00:54:36,800 --> 00:54:39,480
Speaker 4: nothing wrong with the pursuit of a mature deer extending

971
00:54:39,480 --> 00:54:42,759
Speaker 4: out your season, you know, but you need to frame

972
00:54:42,800 --> 00:54:46,799
Speaker 4: it like that, you don't, don't. I only shoot you

973
00:54:46,800 --> 00:54:50,680
Speaker 4: know one seventy plus is that that's we're doing that

974
00:54:50,680 --> 00:54:53,719
Speaker 4: to ourselves, and that's not the scoring system. That's our emphasis.

975
00:54:54,520 --> 00:54:56,279
Speaker 4: And so you know, anytime I get to have a

976
00:54:56,320 --> 00:54:59,840
Speaker 4: conversation with anyone, I mean that's why I came up

977
00:54:59,840 --> 00:55:02,280
Speaker 4: to you. I saw your article on the antler scoring.

978
00:55:02,320 --> 00:55:04,120
Speaker 4: I'm like, man, he's got some good points, but there's

979
00:55:04,160 --> 00:55:07,600
Speaker 4: also some counterpoints here. Let's have this discussion, you know,

980
00:55:07,760 --> 00:55:09,600
Speaker 4: let's make ourselves better as a community.

981
00:55:09,719 --> 00:55:11,120
Speaker 3: And that's my number one concern.

982
00:55:11,360 --> 00:55:14,960
Speaker 4: I personally, I think if Boone and Crockett and Pope

983
00:55:14,960 --> 00:55:18,799
Speaker 4: and Young ceased keeping records tomorrow, both organizations would continue.

984
00:55:19,440 --> 00:55:21,279
Speaker 4: I don't think it would do a darn thing, though,

985
00:55:21,320 --> 00:55:23,200
Speaker 4: because there's a system and people are still going to

986
00:55:23,239 --> 00:55:26,919
Speaker 4: measure antlers, and then there's just one less referee in there,

987
00:55:27,080 --> 00:55:29,040
Speaker 4: so abandoning in it. I don't think does this any

988
00:55:29,080 --> 00:55:32,000
Speaker 4: good either. It's already been it's already been done. We've

989
00:55:32,040 --> 00:55:34,759
Speaker 4: put our emphasis on a number and not the experience.

990
00:55:36,280 --> 00:55:39,080
Speaker 4: You know, that's my concern. That's what I lay awake

991
00:55:39,160 --> 00:55:41,080
Speaker 4: staring at the ceiling at night, thinking about how do

992
00:55:41,160 --> 00:55:44,840
Speaker 4: I address this, you know, to maintain our conservation successes

993
00:55:44,880 --> 00:55:46,800
Speaker 4: that we work so hard to get into the future.

994
00:55:49,520 --> 00:55:51,799
Speaker 2: Yeah, what do you think about that? John?

995
00:55:53,200 --> 00:55:56,680
Speaker 5: I I don't think it should be about the score.

996
00:55:56,840 --> 00:55:59,440
Speaker 5: I don't think it should be about the competition. I

997
00:55:59,680 --> 00:56:04,680
Speaker 5: think the hunters out there that use it as a

998
00:56:04,719 --> 00:56:12,120
Speaker 5: measuring tool just to want to our just that's their priority.

999
00:56:12,440 --> 00:56:16,280
Speaker 5: This is an emotional issue for them, and value based

1000
00:56:16,360 --> 00:56:21,000
Speaker 5: judgment on how they want to recognize that animal. It

1001
00:56:21,080 --> 00:56:25,480
Speaker 5: worries me. I think it's divisive. I am very pro

1002
00:56:25,560 --> 00:56:28,400
Speaker 5: scoring for the reasons that I have mentioned in the

1003
00:56:28,480 --> 00:56:32,279
Speaker 5: last hour. I think we've got to be careful. I

1004
00:56:32,360 --> 00:56:34,400
Speaker 5: read your article and I wondered if this was a

1005
00:56:34,400 --> 00:56:37,520
Speaker 5: discussion we would have had ten years ago or fifteen

1006
00:56:37,600 --> 00:56:44,040
Speaker 5: years ago, before everyone was so connected and so the

1007
00:56:44,160 --> 00:56:48,360
Speaker 5: ability to comment on everything was a bit more restrictive

1008
00:56:48,400 --> 00:56:50,600
Speaker 5: if you were writing a letter to the editor of

1009
00:56:50,640 --> 00:56:53,280
Speaker 5: Field and Stream or Outdoor Life or something about that

1010
00:56:53,719 --> 00:56:55,839
Speaker 5: when a score was mentioned. I mean, now it's all

1011
00:56:55,880 --> 00:56:59,840
Speaker 5: in real time, and so it worries me. Emphasizing this

1012
00:57:00,000 --> 00:57:03,919
Speaker 5: score and nothing else. I think it's going to take

1013
00:57:03,960 --> 00:57:06,200
Speaker 5: away from what this was put in place to do

1014
00:57:06,880 --> 00:57:10,040
Speaker 5: and take away from the potential that it has, and

1015
00:57:10,120 --> 00:57:15,080
Speaker 5: so that will fall on deaf ears to some hunters.

1016
00:57:15,239 --> 00:57:19,880
Speaker 5: I recognize that fully, and that's just my personal perspective.

1017
00:57:22,080 --> 00:57:24,800
Speaker 2: So this is a question that I have discussed with

1018
00:57:25,280 --> 00:57:30,680
Speaker 2: all of our past guests within this mini series. I

1019
00:57:30,720 --> 00:57:34,080
Speaker 2: think a lot of us have similar worries and concerns

1020
00:57:34,120 --> 00:57:38,040
Speaker 2: similar to what both of you just described. But the

1021
00:57:38,120 --> 00:57:41,160
Speaker 2: question is always, but what do we do about it?

1022
00:57:42,160 --> 00:57:45,960
Speaker 2: Because I think you mentioned this earlier justin right, the

1023
00:57:46,400 --> 00:57:48,960
Speaker 2: content that people click on, the content that people want

1024
00:57:49,000 --> 00:57:52,000
Speaker 2: to watch on YouTube or see on Instagram or read

1025
00:57:52,040 --> 00:57:54,800
Speaker 2: from the Boone and Crocker Club. You know, it's maybe

1026
00:57:55,200 --> 00:57:58,440
Speaker 2: five percent of the people will read the article about

1027
00:57:58,520 --> 00:58:02,040
Speaker 2: the conservation story related to this data, but a million

1028
00:58:02,080 --> 00:58:05,520
Speaker 2: people will read the article about the new world record.

1029
00:58:07,720 --> 00:58:11,120
Speaker 2: Everybody will click the YouTube thumbnail with a two hundred

1030
00:58:11,120 --> 00:58:26,440
Speaker 2: and seventy inch double drop time mega giant. Right, So

1031
00:58:26,480 --> 00:58:30,120
Speaker 2: the question is, like, how do we change this thing

1032
00:58:31,080 --> 00:58:34,960
Speaker 2: that just is I guess as human nature related to

1033
00:58:34,960 --> 00:58:42,120
Speaker 2: some degree also cultural inertia. If we all know that

1034
00:58:42,160 --> 00:58:45,320
Speaker 2: there's something I don't know if toxic's the right word,

1035
00:58:45,400 --> 00:58:50,240
Speaker 2: but something that seems unhealthy within it. If we know

1036
00:58:50,320 --> 00:58:54,480
Speaker 2: there's something there, what kinds of things can we tangibly

1037
00:58:54,600 --> 00:58:58,240
Speaker 2: do as individuals or as leaders within this community, or

1038
00:58:58,320 --> 00:59:03,520
Speaker 2: communicators or whatever role anyone listening plays within this hunting community.

1039
00:59:04,960 --> 00:59:08,080
Speaker 2: What kinds of things would the two of you suggest

1040
00:59:08,560 --> 00:59:11,840
Speaker 2: an individual do to be a part of changing this

1041
00:59:12,480 --> 00:59:15,440
Speaker 2: trend in a little bit more positive a direction.

1042
00:59:16,720 --> 00:59:19,040
Speaker 5: I think individuals need to be prepared to speak up.

1043
00:59:19,360 --> 00:59:23,920
Speaker 5: I mean, hopefully we could generate some momentum through our

1044
00:59:23,960 --> 00:59:27,920
Speaker 5: discussion today and if people understand this is not a

1045
00:59:28,680 --> 00:59:31,240
Speaker 5: tool to figure out how much you can brag about

1046
00:59:31,400 --> 00:59:34,520
Speaker 5: or how much attention or clicks or whatever you can get,

1047
00:59:34,960 --> 00:59:38,400
Speaker 5: but the real meaning of why we're taking these measurements.

1048
00:59:39,080 --> 00:59:45,120
Speaker 5: And if somebody sees that there's a new whatever record

1049
00:59:45,160 --> 00:59:49,160
Speaker 5: it might be for whatever species, emphasize that that's just

1050
00:59:49,240 --> 00:59:52,200
Speaker 5: one data point and that's interesting and we can appreciate

1051
00:59:52,240 --> 00:59:56,440
Speaker 5: that animal. And I'll see any number of articles in

1052
00:59:56,440 --> 00:59:59,680
Speaker 5: the local paper where angler catches a record fish of

1053
00:59:59,720 --> 01:00:02,520
Speaker 5: whatever kind, and yeah, you click on it. It's interesting

1054
01:00:02,560 --> 01:00:06,960
Speaker 5: to know. But communicating why we're taking these messages, I

1055
01:00:07,000 --> 01:00:10,560
Speaker 5: mean measure measurements, and how to message it to them

1056
01:00:11,200 --> 01:00:14,440
Speaker 5: why the community, I think is is a great first step.

1057
01:00:15,160 --> 01:00:21,240
Speaker 4: What you're doing today, What do you think justin you know,

1058
01:00:21,520 --> 01:00:24,080
Speaker 4: I've spent my time in the nonprofit world.

1059
01:00:24,160 --> 01:00:25,800
Speaker 3: You know, do do the words.

1060
01:00:25,520 --> 01:00:27,760
Speaker 4: Of Roosevelt, Do what what you can with what you

1061
01:00:27,840 --> 01:00:31,080
Speaker 4: have where you're at, you know, get on that advisory

1062
01:00:31,120 --> 01:00:34,120
Speaker 4: committee in your local area. You know, get involved with

1063
01:00:34,160 --> 01:00:37,200
Speaker 4: a local conservation group, depending on what it may be,

1064
01:00:37,360 --> 01:00:41,320
Speaker 4: get involved at a national level. You know, somebody has

1065
01:00:41,320 --> 01:00:43,600
Speaker 4: a desire to learn about hunting, or maybe they don't

1066
01:00:43,680 --> 01:00:47,240
Speaker 4: like wild game man, invite them over, tell the tell

1067
01:00:47,280 --> 01:00:49,800
Speaker 4: that conservation story.

1068
01:00:49,840 --> 01:00:51,720
Speaker 3: And I think if.

1069
01:00:54,560 --> 01:00:56,480
Speaker 4: If you, if you put it out there enough and

1070
01:00:56,520 --> 01:00:58,840
Speaker 4: you have enough people that you kind of bring bring

1071
01:00:58,880 --> 01:01:00,680
Speaker 4: along with you and say, hey, you know, if you're

1072
01:01:00,680 --> 01:01:02,880
Speaker 4: gonna be a hunter, I think this is part of

1073
01:01:02,920 --> 01:01:05,040
Speaker 4: it too, not just going out and filling the freezer,

1074
01:01:05,160 --> 01:01:07,600
Speaker 4: hanging the buck on the wall, you know, do a

1075
01:01:07,640 --> 01:01:10,720
Speaker 4: game feed, Invite some friends over that maybe don't understand it,

1076
01:01:10,760 --> 01:01:13,840
Speaker 4: and show them this type of thing, you know, I again,

1077
01:01:14,560 --> 01:01:17,160
Speaker 4: ground up type thing. I mean, that's the that's what

1078
01:01:18,400 --> 01:01:21,720
Speaker 4: the conservation movement always was. That's what P and Y was,

1079
01:01:21,800 --> 01:01:25,520
Speaker 4: that's what B and C was. You know, spread your

1080
01:01:25,520 --> 01:01:28,000
Speaker 4: influence as far as you can. And that's how I

1081
01:01:28,040 --> 01:01:30,960
Speaker 4: think we combat this, you know. I mean, somebody's all

1082
01:01:30,960 --> 01:01:34,120
Speaker 4: excited about, you know, that giant deer that they killed,

1083
01:01:34,160 --> 01:01:35,920
Speaker 4: and then they mentioned, well I didn't get to cutting

1084
01:01:35,920 --> 01:01:38,200
Speaker 4: it up because I was too busy driving it around town.

1085
01:01:38,840 --> 01:01:40,959
Speaker 4: I mean, don't lose a friendship over it, but maybe

1086
01:01:41,040 --> 01:01:43,320
Speaker 4: chastise them a bit of like, hey man, you're you're

1087
01:01:43,400 --> 01:01:45,680
Speaker 4: kind of minimizing it to one thing, you know, we're

1088
01:01:46,600 --> 01:01:48,760
Speaker 4: I mean, I always joke, Yeah, man, I love I

1089
01:01:48,760 --> 01:01:50,680
Speaker 4: love hunting meat, but I also like something to hang

1090
01:01:50,760 --> 01:01:53,840
Speaker 4: my hats on, you know, everything, Every part of that

1091
01:01:53,960 --> 01:01:57,760
Speaker 4: freaking animal should be utilized and respected, you know. And

1092
01:01:57,800 --> 01:02:00,240
Speaker 4: I don't care if you're minimizing it to just the

1093
01:02:00,280 --> 01:02:03,320
Speaker 4: meat or just the antlers, or just any one thing

1094
01:02:03,400 --> 01:02:07,040
Speaker 4: that's bad. And and as hunters, we need we need

1095
01:02:07,080 --> 01:02:09,400
Speaker 4: to take we need to take pride in every single

1096
01:02:09,480 --> 01:02:13,400
Speaker 4: thing that has to do without the exercising, the preparing,

1097
01:02:13,720 --> 01:02:16,880
Speaker 4: the shooting of the bow year round, you know, all

1098
01:02:17,000 --> 01:02:17,680
Speaker 4: that stuff.

1099
01:02:17,800 --> 01:02:18,800
Speaker 3: That's how we fight this.

1100
01:02:19,000 --> 01:02:23,320
Speaker 4: We show that we were brought here by conservation minded people,

1101
01:02:23,400 --> 01:02:25,120
Speaker 4: and we're going to do our part to ensure that

1102
01:02:25,120 --> 01:02:28,240
Speaker 4: that conservation mindset continues and we don't get wrapped up

1103
01:02:28,240 --> 01:02:31,760
Speaker 4: in the commercial are commercialization of killing the biggest at

1104
01:02:31,800 --> 01:02:33,720
Speaker 4: the furthest distance as quick as possible.

1105
01:02:35,320 --> 01:02:41,880
Speaker 2: Yeah. So I I find myself at a fork in

1106
01:02:41,920 --> 01:02:46,400
Speaker 2: the road because something you just said there justin uh,

1107
01:02:46,720 --> 01:02:49,000
Speaker 2: brings to mind another kind of half of this whole

1108
01:02:49,000 --> 01:02:51,840
Speaker 2: cultural discussion that I've been having over the last few weeks.

1109
01:02:52,960 --> 01:02:55,600
Speaker 2: You said something on the lines of, you know, whether

1110
01:02:55,640 --> 01:02:57,880
Speaker 2: you can kill a deer from as far away or

1111
01:02:57,920 --> 01:03:00,880
Speaker 2: as fast as possible? Right And when I hear that,

1112
01:03:00,960 --> 01:03:04,440
Speaker 2: I'm thinking all of these changes in technology and how

1113
01:03:04,440 --> 01:03:09,040
Speaker 2: that's impacting how we hunt, whether that be the long

1114
01:03:09,080 --> 01:03:14,160
Speaker 2: distance shooting craze, whether that be thermal optics, live streaming,

1115
01:03:14,320 --> 01:03:19,000
Speaker 2: cell cameras, drones being used, all of these electronic optics

1116
01:03:19,000 --> 01:03:22,840
Speaker 2: that allow you to range, find and have all your

1117
01:03:22,960 --> 01:03:26,400
Speaker 2: adjustments made automatically for you. There's so many things changing

1118
01:03:26,480 --> 01:03:33,160
Speaker 2: right now that are impacting really what hunting is. And

1119
01:03:33,200 --> 01:03:35,960
Speaker 2: I have so many questions, so many concerns about that,

1120
01:03:36,960 --> 01:03:39,920
Speaker 2: and I think a lot of hunters do. But at

1121
01:03:39,960 --> 01:03:45,160
Speaker 2: the same time, there's this whole pushback on that from

1122
01:03:45,200 --> 01:03:49,080
Speaker 2: within the hunting community, which is anytime somebody says, well,

1123
01:03:49,120 --> 01:03:50,880
Speaker 2: I don't know about this technology or I don't know

1124
01:03:50,880 --> 01:03:53,960
Speaker 2: about this thing or that thing, the automatic response is

1125
01:03:55,360 --> 01:03:58,200
Speaker 2: you're tearing down others in the hunting community. You're dividing

1126
01:03:58,200 --> 01:04:01,680
Speaker 2: the hunting community. You shouldn't do that at all. Everything

1127
01:04:01,720 --> 01:04:06,120
Speaker 2: should be okay as long as it's legal. What is

1128
01:04:06,160 --> 01:04:10,080
Speaker 2: your thought on the trends as we see them now

1129
01:04:10,280 --> 01:04:13,600
Speaker 2: with technology and gear justin I'm sure this is something

1130
01:04:13,640 --> 01:04:16,320
Speaker 2: that you know, you're discussing at Boon and Crockett on

1131
01:04:16,320 --> 01:04:17,920
Speaker 2: the Ethics Committee a lot, and I'm sure at P

1132
01:04:18,000 --> 01:04:21,120
Speaker 2: and Y you're thinking about it a lot. How are

1133
01:04:21,160 --> 01:04:23,840
Speaker 2: you individually and how is P and Y as an

1134
01:04:23,880 --> 01:04:29,600
Speaker 2: organization thinking about the what seems like a rampant speed

1135
01:04:29,720 --> 01:04:32,520
Speaker 2: up of the advance of technology and how it's impacting.

1136
01:04:32,200 --> 01:04:36,200
Speaker 4: Hunting, you know, and it is and it's realistically you know,

1137
01:04:36,640 --> 01:04:39,160
Speaker 4: put this into context. It feels like, yeah, we're getting

1138
01:04:39,240 --> 01:04:40,520
Speaker 4: a ton of stuff thrown.

1139
01:04:40,320 --> 01:04:41,080
Speaker 3: At us right now.

1140
01:04:42,120 --> 01:04:45,240
Speaker 4: But this this idea of technology and giving the animal

1141
01:04:45,240 --> 01:04:49,439
Speaker 4: an opportunity to to win, for lack of a better term,

1142
01:04:49,480 --> 01:04:51,560
Speaker 4: I mean that is the root of fair chase. The

1143
01:04:51,560 --> 01:04:55,439
Speaker 4: animal has the has a fair chance to come out

1144
01:04:55,520 --> 01:04:59,919
Speaker 4: victorious in in the in the competition between hunter and hunt.

1145
01:05:00,240 --> 01:05:02,720
Speaker 3: Right. You know, this goes back.

1146
01:05:02,800 --> 01:05:05,360
Speaker 4: The very first time this came up was when Boone

1147
01:05:05,360 --> 01:05:09,040
Speaker 4: and Crockett required fair chase. Was the sixties with the

1148
01:05:09,080 --> 01:05:13,000
Speaker 4: prevalence of airplane usage in Alaska for scouting, and that

1149
01:05:13,200 --> 01:05:15,439
Speaker 4: was the first time that the organization was like, Noah,

1150
01:05:15,800 --> 01:05:17,959
Speaker 4: that could get real bad if people just flew around,

1151
01:05:18,080 --> 01:05:20,400
Speaker 4: spotted them from the air, landed and shot them like

1152
01:05:20,480 --> 01:05:23,680
Speaker 4: that's that's no good like that, that's not giving the

1153
01:05:23,720 --> 01:05:28,040
Speaker 4: animal a chance to escape or evade hunters. And obviously

1154
01:05:28,120 --> 01:05:31,840
Speaker 4: we've come further and further along and technology is not

1155
01:05:32,440 --> 01:05:35,880
Speaker 4: inherently the devil. I mean, there's there's things that we

1156
01:05:36,000 --> 01:05:39,200
Speaker 4: have now that that I would argue have have made

1157
01:05:39,280 --> 01:05:41,880
Speaker 4: us more ethical hunters, more legal hunters. You know, now

1158
01:05:41,880 --> 01:05:45,160
Speaker 4: I know exactly where I'm standing. I can you know,

1159
01:05:45,200 --> 01:05:48,240
Speaker 4: I can figure stuff out that before like, okay, well,

1160
01:05:48,240 --> 01:05:50,960
Speaker 4: best guess I'm pretty sure that fence line separates this

1161
01:05:51,040 --> 01:05:51,320
Speaker 4: and this.

1162
01:05:51,400 --> 01:05:53,640
Speaker 3: So that type of thing I think is good.

1163
01:05:53,680 --> 01:05:56,480
Speaker 4: But what we need to do, and this is the

1164
01:05:56,520 --> 01:05:59,040
Speaker 4: discussions that are had with with both Pope and Young

1165
01:05:59,080 --> 01:06:01,320
Speaker 4: and Boone and Crockett. Obviously a little bit different in

1166
01:06:01,400 --> 01:06:05,280
Speaker 4: terms of Pope and Young's mission is promotion of archery,

1167
01:06:05,480 --> 01:06:10,400
Speaker 4: and they have defined archery as a vertical bow. You know,

1168
01:06:10,640 --> 01:06:14,800
Speaker 4: it's a valid method of harvest. That's why we were

1169
01:06:14,840 --> 01:06:18,160
Speaker 4: started was to prove it was. And so they have

1170
01:06:18,320 --> 01:06:22,080
Speaker 4: a little bit tighter focus on looking at some of

1171
01:06:22,080 --> 01:06:24,960
Speaker 4: the technologies. Is this technology going to be detrimental to

1172
01:06:25,000 --> 01:06:29,160
Speaker 4: the wildlife or does this make a bow no longer

1173
01:06:29,200 --> 01:06:31,760
Speaker 4: a bow? Right, So there is some differences there between

1174
01:06:31,760 --> 01:06:34,600
Speaker 4: the two groups, but I mean, at the end of

1175
01:06:34,640 --> 01:06:38,840
Speaker 4: the day, like you're not supposed to always be successful.

1176
01:06:38,960 --> 01:06:43,520
Speaker 4: Hunting is not supposed to have a predetermined outcome like

1177
01:06:44,560 --> 01:06:46,360
Speaker 4: you know, I mean those of us that have been

1178
01:06:46,400 --> 01:06:50,320
Speaker 4: doing it forever, Like what relives in your head again

1179
01:06:50,400 --> 01:06:52,439
Speaker 4: and again the time you made a mistake, the time

1180
01:06:52,480 --> 01:06:54,640
Speaker 4: that big buck winded you, the time you moved when

1181
01:06:54,680 --> 01:06:56,440
Speaker 4: you should know if you tried to draw and he

1182
01:06:56,520 --> 01:06:59,400
Speaker 4: busted you. You know, those failures are the ones that

1183
01:06:59,440 --> 01:07:01,080
Speaker 4: are burned in to your head and make you a

1184
01:07:01,120 --> 01:07:07,840
Speaker 4: better hunter. And when we start pushing technology to well,

1185
01:07:08,080 --> 01:07:09,840
Speaker 4: he can't smell me now because I don't have to

1186
01:07:09,880 --> 01:07:12,160
Speaker 4: go into that betting area for six months because I

1187
01:07:12,160 --> 01:07:16,040
Speaker 4: have a cellular trailer camera. Yes, it's made it easier. Yes,

1188
01:07:16,120 --> 01:07:19,000
Speaker 4: the deer's not as disturbed. I understand those arguments. But

1189
01:07:20,440 --> 01:07:22,920
Speaker 4: as a hunter, isn't that part of the game of

1190
01:07:23,000 --> 01:07:25,720
Speaker 4: sneaking in there, finding that betting area and doing all

1191
01:07:25,760 --> 01:07:30,439
Speaker 4: that without relying on technology. And it's a very it's

1192
01:07:30,440 --> 01:07:38,400
Speaker 4: a very volatile debate. I mean what's inappropriate to me

1193
01:07:38,480 --> 01:07:41,200
Speaker 4: to the next game. The next guy might just be fine.

1194
01:07:41,920 --> 01:07:45,200
Speaker 4: And so both organizations find ourselves in this situation where

1195
01:07:45,200 --> 01:07:47,520
Speaker 4: we're trying to play referee, but trying to get it

1196
01:07:47,560 --> 01:07:50,960
Speaker 4: to a higher level. What we found is addressing each

1197
01:07:51,040 --> 01:07:54,880
Speaker 4: technology one by one. In essence, we're picking winners and

1198
01:07:54,920 --> 01:07:58,240
Speaker 4: losers of companies, which is never what this was supposed

1199
01:07:58,280 --> 01:08:01,080
Speaker 4: to be doing. Not like, oh, they invested this much,

1200
01:08:01,120 --> 01:08:03,240
Speaker 4: but we don't think this is fair chase. That's not

1201
01:08:03,560 --> 01:08:05,600
Speaker 4: at all what any of these groups are ever trying

1202
01:08:05,600 --> 01:08:08,439
Speaker 4: to say. So we're trying to say from a higher level,

1203
01:08:08,520 --> 01:08:12,760
Speaker 4: what is the line that crossed the technology crosses that

1204
01:08:12,880 --> 01:08:17,479
Speaker 4: makes it unfair? One that comes up with B and

1205
01:08:17,520 --> 01:08:18,120
Speaker 4: C a lot.

1206
01:08:18,720 --> 01:08:19,639
Speaker 3: Is it giving me.

1207
01:08:19,680 --> 01:08:24,320
Speaker 4: The location of game electronically that I wouldn't have got otherwise.

1208
01:08:25,000 --> 01:08:25,759
Speaker 3: Think drones.

1209
01:08:26,560 --> 01:08:30,840
Speaker 4: Think some states now through freedom of information, you have

1210
01:08:30,920 --> 01:08:34,960
Speaker 4: to release GPS coordinates, real time GPS coordinates of where

1211
01:08:34,960 --> 01:08:37,200
Speaker 4: a herd elk is in collar data. They have to

1212
01:08:37,240 --> 01:08:41,799
Speaker 4: release that. You're now getting that actual location of the animal.

1213
01:08:42,520 --> 01:08:46,040
Speaker 4: And so how do we make these high level rules

1214
01:08:46,920 --> 01:08:50,080
Speaker 4: that folks can look at and say, you know, is

1215
01:08:50,120 --> 01:08:52,760
Speaker 4: this crossing the line and the answer is there's not

1216
01:08:52,840 --> 01:08:55,240
Speaker 4: a definitive line. That's why Boone and Crockett and Pope

1217
01:08:55,240 --> 01:08:58,200
Speaker 4: Young takes so much heat is we're trying to find

1218
01:08:58,240 --> 01:09:02,040
Speaker 4: that great line and listen everybody's perspective, and we're the

1219
01:09:02,040 --> 01:09:04,639
Speaker 4: ones that finally sometimes have to say no, man, that's.

1220
01:09:04,520 --> 01:09:06,120
Speaker 3: Just too far, and we have to say.

1221
01:09:05,960 --> 01:09:12,599
Speaker 2: It, yeah, John, What do you feel on this topic?

1222
01:09:12,640 --> 01:09:12,920
Speaker 3: Person?

1223
01:09:12,960 --> 01:09:15,360
Speaker 5: It worries me. I mean, I agree with Justin and

1224
01:09:15,520 --> 01:09:20,000
Speaker 5: innovation is coming, and this is so tied to personal

1225
01:09:20,960 --> 01:09:23,640
Speaker 5: ethics and values and priorities that.

1226
01:09:23,760 --> 01:09:25,599
Speaker 3: It's it's.

1227
01:09:27,080 --> 01:09:31,599
Speaker 5: An individual's decision what they think crosses an ethical line

1228
01:09:31,600 --> 01:09:33,200
Speaker 5: when it comes down to it, and so for some

1229
01:09:33,280 --> 01:09:38,200
Speaker 5: people it's no big deal. For me, I think that

1230
01:09:39,960 --> 01:09:42,599
Speaker 5: some of this new technology is just making it too

1231
01:09:44,000 --> 01:09:50,160
Speaker 5: efficient to harvest gain from a population level among hunters.

1232
01:09:50,640 --> 01:09:52,639
Speaker 5: I mean, one thing that worries me is I hunt

1233
01:09:52,640 --> 01:09:56,800
Speaker 5: throughout the West. You look at species like mule deer

1234
01:09:56,840 --> 01:10:01,400
Speaker 5: and elk where units will have, however many tags allocated

1235
01:10:02,320 --> 01:10:08,240
Speaker 5: the game agencies are buffering where that allocation is not

1236
01:10:08,360 --> 01:10:13,559
Speaker 5: assuming one hundred percent success among hunters. If these new

1237
01:10:13,640 --> 01:10:19,120
Speaker 5: technologies come in and boost success that high, then tag

1238
01:10:19,160 --> 01:10:21,080
Speaker 5: allocation for all the rest of us who are not

1239
01:10:21,200 --> 01:10:24,439
Speaker 5: using those technologies may be reduced and so it may

1240
01:10:24,560 --> 01:10:26,960
Speaker 5: end up biting us in the butt and making that

1241
01:10:27,280 --> 01:10:32,200
Speaker 5: tag more difficult to draw. I've heard that animals will evolve,

1242
01:10:32,360 --> 01:10:36,920
Speaker 5: and you know, just like mallards now get a spinning

1243
01:10:36,960 --> 01:10:39,839
Speaker 5: when decoy a bit more than they did twenty years ago,

1244
01:10:40,680 --> 01:10:46,240
Speaker 5: stuff like thermal units drones. That's that's a pretty tricky

1245
01:10:46,320 --> 01:10:51,360
Speaker 5: thing to evolve to respond to elk And so I

1246
01:10:51,840 --> 01:10:52,799
Speaker 5: don't see that happening.

1247
01:10:54,600 --> 01:10:59,920
Speaker 2: Yeah, So so one idea that I've had, and it

1248
01:11:00,120 --> 01:11:03,519
Speaker 2: might be Pie in the Sky and Pollyanna. So I

1249
01:11:03,600 --> 01:11:06,000
Speaker 2: guess what I'm looking for here is is like a

1250
01:11:06,040 --> 01:11:08,559
Speaker 2: BS check from the two of you if you think

1251
01:11:08,600 --> 01:11:12,160
Speaker 2: anybody would go for this. But but when it comes

1252
01:11:12,200 --> 01:11:14,920
Speaker 2: to this whole technology thing, right, as we've we've all

1253
01:11:15,000 --> 01:11:17,720
Speaker 2: kind of admitted it's it's very personal. It's very subjective.

1254
01:11:17,760 --> 01:11:20,759
Speaker 2: Everyone's going to draw align the sand in a different place,

1255
01:11:21,400 --> 01:11:25,000
Speaker 2: and that is that's by the nature of what ethics are, right.

1256
01:11:25,040 --> 01:11:28,920
Speaker 2: We all have a different comfort level with different things.

1257
01:11:29,960 --> 01:11:35,599
Speaker 2: But it seems like there are some general lines where

1258
01:11:35,640 --> 01:11:37,960
Speaker 2: you start getting closer and closer to and and most

1259
01:11:38,000 --> 01:11:42,040
Speaker 2: folks start feeling like, ah, there's something going on here.

1260
01:11:42,520 --> 01:11:45,240
Speaker 2: So there's there's two hypothetical ways you could deal with

1261
01:11:45,280 --> 01:11:48,640
Speaker 2: something like this. There's the there's the way of you know,

1262
01:11:49,439 --> 01:11:52,639
Speaker 2: using the stick and saying we're going to ban this thing,

1263
01:11:52,760 --> 01:11:54,800
Speaker 2: or we're going to regulate this thing, or we are

1264
01:11:54,840 --> 01:11:57,760
Speaker 2: going to outlaw this thing, or say this thing you

1265
01:11:57,760 --> 01:12:01,120
Speaker 2: know can't be allowed in the record books, et cetera.

1266
01:12:01,760 --> 01:12:05,080
Speaker 2: On the flip side, there's this alternative way, which could

1267
01:12:05,120 --> 01:12:13,760
Speaker 2: be promoting the alternative or popularizing the antithesis. So one

1268
01:12:13,760 --> 01:12:15,920
Speaker 2: thought I had is so for me, one of my

1269
01:12:16,080 --> 01:12:20,479
Speaker 2: lines in the sand is with cell cameras. I've always

1270
01:12:20,520 --> 01:12:23,599
Speaker 2: thought that, like real time data is a line too

1271
01:12:23,640 --> 01:12:27,280
Speaker 2: far from me. So I personally set a twenty four

1272
01:12:27,280 --> 01:12:30,880
Speaker 2: hour delay on all cell cameras so that I'm a

1273
01:12:31,120 --> 01:12:34,360
Speaker 2: full twenty four hours removed from any knowledge that I

1274
01:12:34,439 --> 01:12:37,639
Speaker 2: gained through that technology. And maybe even that's too much,

1275
01:12:37,840 --> 01:12:40,679
Speaker 2: but right now that's where I personally have drawn my line,

1276
01:12:41,800 --> 01:12:46,120
Speaker 2: and I've thought, what if you could establish some kind

1277
01:12:46,160 --> 01:12:49,799
Speaker 2: of light line like that, where it's like a twenty

1278
01:12:49,840 --> 01:12:53,960
Speaker 2: four hour delay on a trial camera, for example, would

1279
01:12:54,000 --> 01:12:57,160
Speaker 2: get that trail camera a fair chase certification from the

1280
01:12:57,160 --> 01:13:00,280
Speaker 2: Boon and Crocket Club or from Pope and Young and

1281
01:13:00,320 --> 01:13:02,640
Speaker 2: that could be like a stamp, like a badge of

1282
01:13:02,680 --> 01:13:06,040
Speaker 2: honor that a company could use to help market their product.

1283
01:13:06,320 --> 01:13:09,800
Speaker 2: Or what if we had a I don't know what

1284
01:13:09,840 --> 01:13:13,479
Speaker 2: another example would be, but another one of these possible

1285
01:13:13,520 --> 01:13:16,960
Speaker 2: game changers. I don't know, I've, for lack of a

1286
01:13:17,120 --> 01:13:19,080
Speaker 2: better more thought on this, We'll stick with a trail

1287
01:13:19,120 --> 01:13:22,240
Speaker 2: camera example. What if there's something that where we could

1288
01:13:22,320 --> 01:13:26,080
Speaker 2: vote with our dollars. Hunters could say like, hey, we

1289
01:13:26,160 --> 01:13:29,920
Speaker 2: do want to promote this idea of keeping things, you know,

1290
01:13:30,040 --> 01:13:32,040
Speaker 2: not always getting easier, but I actually we want to

1291
01:13:32,080 --> 01:13:34,040
Speaker 2: tap the brakes on this thing, and we want to

1292
01:13:34,120 --> 01:13:37,160
Speaker 2: show all that desire we have by voting with our

1293
01:13:37,200 --> 01:13:41,240
Speaker 2: dollars for the things that actually make it harder. Is

1294
01:13:41,240 --> 01:13:43,000
Speaker 2: that the kind of thing that would ever work? Is

1295
01:13:43,040 --> 01:13:45,240
Speaker 2: that an idea like God, I would buy all fair

1296
01:13:45,320 --> 01:13:48,120
Speaker 2: Chase certified cell cameras. Then I could be like, hey, yeah,

1297
01:13:48,240 --> 01:13:50,920
Speaker 2: I still get to enjoy some benefits of this technology,

1298
01:13:51,040 --> 01:13:56,120
Speaker 2: but I am you know, I am by by by

1299
01:13:56,160 --> 01:13:58,880
Speaker 2: the boon of the technology itself limited to not giving

1300
01:13:58,880 --> 01:14:03,640
Speaker 2: into the temptation of using everything. Is that kind of

1301
01:14:03,680 --> 01:14:06,120
Speaker 2: thing A model maybe that could work in the future

1302
01:14:06,240 --> 01:14:09,639
Speaker 2: with other technologies as we go forward, am I onto something?

1303
01:14:10,240 --> 01:14:11,920
Speaker 3: I completely agreed with you.

1304
01:14:11,960 --> 01:14:17,679
Speaker 4: And back before the the cellular camera got the following

1305
01:14:17,680 --> 01:14:19,840
Speaker 4: that it had, and you know, eighty percent of them

1306
01:14:19,840 --> 01:14:22,920
Speaker 4: went that way. You know, I presented that to some

1307
01:14:22,960 --> 01:14:26,120
Speaker 4: companies and I said, hey, man, what if we put

1308
01:14:26,400 --> 01:14:30,920
Speaker 4: a fair chase certified mark on that? And there wasn't

1309
01:14:31,360 --> 01:14:33,880
Speaker 4: from the industry. The companies I talked to at the

1310
01:14:33,960 --> 01:14:39,000
Speaker 4: time didn't really gravitate towards that. It didn't it didn't

1311
01:14:39,040 --> 01:14:41,400
Speaker 4: have the appeal that they wanted it to. Now that

1312
01:14:41,479 --> 01:14:43,600
Speaker 4: goes back to what we talked about in scoring and

1313
01:14:43,640 --> 01:14:47,439
Speaker 4: the adoption of you know, everybody taking part in this

1314
01:14:47,560 --> 01:14:50,360
Speaker 4: record book. You know, a component of that is an

1315
01:14:50,360 --> 01:14:53,040
Speaker 4: ethical piece. All these animals have to be taken in

1316
01:14:53,080 --> 01:14:55,960
Speaker 4: fair chase. They do you do have to say I

1317
01:14:56,000 --> 01:14:59,479
Speaker 4: took them within these rules, which is the rules we're making.

1318
01:14:59,600 --> 01:15:02,559
Speaker 4: And so again it's just buying. I mean I would

1319
01:15:02,680 --> 01:15:04,720
Speaker 4: I'd love to go to a company and say, hey,

1320
01:15:04,800 --> 01:15:08,120
Speaker 4: let's let's cross promote something. I've reached out to some

1321
01:15:08,160 --> 01:15:12,200
Speaker 4: trail cam companies and some other technologies. My thought has been, hey,

1322
01:15:12,920 --> 01:15:15,439
Speaker 4: what if we come out with the company saying yes,

1323
01:15:15,560 --> 01:15:19,040
Speaker 4: this technology is available, Yes it can be used correctly,

1324
01:15:19,120 --> 01:15:21,000
Speaker 4: but also it could be used incorrectly.

1325
01:15:21,479 --> 01:15:24,240
Speaker 3: And if that's said jointly by the producer.

1326
01:15:23,800 --> 01:15:27,439
Speaker 4: And the folks using it, does that not lend some

1327
01:15:27,479 --> 01:15:31,160
Speaker 4: credibility to like, hey, man, let's let's back it down

1328
01:15:31,200 --> 01:15:33,799
Speaker 4: a little bit on this thermal usage, you know, looking

1329
01:15:33,840 --> 01:15:37,160
Speaker 4: over an entire canyon, Like, yeah, you can use it

1330
01:15:37,200 --> 01:15:39,519
Speaker 4: at night. It's good for predator control. It's you know,

1331
01:15:39,600 --> 01:15:42,960
Speaker 4: you could use it here appropriately, but you could also

1332
01:15:43,000 --> 01:15:45,360
Speaker 4: abuse this that way. You're not telling people like you

1333
01:15:45,439 --> 01:15:48,479
Speaker 4: can't do this, but you are saying, like, hey, for

1334
01:15:48,560 --> 01:15:52,400
Speaker 4: the good of hunting, for the survival of species, like

1335
01:15:52,479 --> 01:15:54,800
Speaker 4: there is a way we need to conduct ourselves to

1336
01:15:54,800 --> 01:15:57,400
Speaker 4: give the animals a chance to win. And then we

1337
01:15:57,479 --> 01:16:00,000
Speaker 4: also need to you know, and I'm guilty of it too.

1338
01:16:00,040 --> 01:16:02,479
Speaker 4: You don't post on you know, Facebook, Oh I failed

1339
01:16:02,520 --> 01:16:06,400
Speaker 4: tag soup this year. Man, we gotta be okay with

1340
01:16:06,439 --> 01:16:08,760
Speaker 4: that failure because you tried to make it too hard

1341
01:16:08,880 --> 01:16:10,519
Speaker 4: is just as admirable as success.

1342
01:16:12,920 --> 01:16:17,479
Speaker 2: Yeah, yeah, I really liked that idea of you know,

1343
01:16:17,760 --> 01:16:20,400
Speaker 2: I think I think there's a business opportunity for companies

1344
01:16:20,400 --> 01:16:24,160
Speaker 2: out there to lean into this and there's a there's

1345
01:16:24,160 --> 01:16:27,120
Speaker 2: a there's a story that will sell that will sell products.

1346
01:16:27,160 --> 01:16:29,679
Speaker 2: I really do believe it. Not everybody in the hunting

1347
01:16:29,720 --> 01:16:35,360
Speaker 2: world wants uh the next big technology thing. And even

1348
01:16:35,400 --> 01:16:37,760
Speaker 2: though I know the dollars tell it otherwise, I really

1349
01:16:37,840 --> 01:16:40,240
Speaker 2: do believe there's something here that someone could take advantage of.

1350
01:16:41,960 --> 01:16:44,880
Speaker 2: But it's uh Now, it's above my pay grade, I guess,

1351
01:16:44,960 --> 01:16:47,679
Speaker 2: is what I'm getting at. Do you have any comments

1352
01:16:47,720 --> 01:16:48,640
Speaker 2: on that, John.

1353
01:16:48,600 --> 01:16:52,800
Speaker 5: I'd love to see it work. I mean, I think

1354
01:16:52,840 --> 01:16:56,320
Speaker 5: you're right that it would apply to some people. Make things.

1355
01:16:56,360 --> 01:17:01,680
Speaker 5: Amit improvement. Even if a handful of people switched to

1356
01:17:01,760 --> 01:17:06,080
Speaker 5: that more fair chase approach, that's good for hunting, that's

1357
01:17:06,080 --> 01:17:08,760
Speaker 5: good for all of us. But there's there's going to

1358
01:17:08,800 --> 01:17:12,120
Speaker 5: be hunters out there who are hunting for Instagram and

1359
01:17:13,160 --> 01:17:16,639
Speaker 5: by any means necessary, and I think that that that

1360
01:17:16,760 --> 01:17:18,280
Speaker 5: will be the tough market to reach.

1361
01:17:20,080 --> 01:17:22,280
Speaker 3: I like the idea so reluctant.

1362
01:17:24,600 --> 01:17:27,439
Speaker 2: Well, I'm cross my fingers and hope that somebody at

1363
01:17:27,439 --> 01:17:30,240
Speaker 2: some company is listening and willing to take a risk.

1364
01:17:30,840 --> 01:17:37,360
Speaker 2: To take a chance, guys, take a chance. All right, Well,

1365
01:17:37,400 --> 01:17:41,200
Speaker 2: I think we could continue for two more hours down

1366
01:17:41,439 --> 01:17:44,679
Speaker 2: the road of all of these questions and worries, which

1367
01:17:44,720 --> 01:17:47,479
Speaker 2: I'm guilty of sometimes doing. I'm going to try to

1368
01:17:48,400 --> 01:17:51,960
Speaker 2: save us from that now by offerings and offer am

1369
01:17:53,240 --> 01:17:58,280
Speaker 2: Is there any any other resource, whether it be a

1370
01:17:58,320 --> 01:18:05,160
Speaker 2: book or a documentary or an online essay, or something

1371
01:18:05,600 --> 01:18:11,680
Speaker 2: that either one of you have found inspiration from, or

1372
01:18:11,800 --> 01:18:17,559
Speaker 2: have learned from, or could see value in someone today

1373
01:18:19,439 --> 01:18:22,479
Speaker 2: checking out as a kind of coda to what we've

1374
01:18:22,479 --> 01:18:26,000
Speaker 2: talked about today, Is there any favorite resource or recommend

1375
01:18:26,120 --> 01:18:29,560
Speaker 2: recommendation you could share with the folks listening related to

1376
01:18:29,600 --> 01:18:32,160
Speaker 2: the topics we've covered today, whether that be the history

1377
01:18:32,200 --> 01:18:36,160
Speaker 2: of the scoring system, or conservation and citizens science, or

1378
01:18:36,560 --> 01:18:40,519
Speaker 2: fair chase, or the use or moderation of technology, any

1379
01:18:40,560 --> 01:18:43,599
Speaker 2: of these things we've covered. Is there anything that comes

1380
01:18:43,640 --> 01:18:45,920
Speaker 2: to mind that we maybe should check out?

1381
01:18:46,000 --> 01:18:46,240
Speaker 4: You know what?

1382
01:18:46,360 --> 01:18:49,040
Speaker 2: Really just enough or if either one of you one if.

1383
01:18:48,960 --> 01:18:52,320
Speaker 4: It really depends on how far you know, how deep

1384
01:18:52,360 --> 01:18:55,800
Speaker 4: that rabbit hole you want to go. There's some tremendous

1385
01:18:55,840 --> 01:19:02,040
Speaker 4: reading the biography of Theodore Roosevelt. What what I found

1386
01:19:02,040 --> 01:19:05,560
Speaker 4: super intriguing was that his uncle actually started in fisheries.

1387
01:19:05,600 --> 01:19:09,559
Speaker 4: So a lot of our conservation movement originally was based

1388
01:19:09,600 --> 01:19:13,840
Speaker 4: off of the hatchery model. You know that that one's

1389
01:19:13,880 --> 01:19:16,360
Speaker 4: like the ultimate, like trying to answer the question of

1390
01:19:16,400 --> 01:19:18,200
Speaker 4: how do we get where we were? How did you know?

1391
01:19:18,240 --> 01:19:22,080
Speaker 4: How did this land? Just straight for technology? We just

1392
01:19:22,600 --> 01:19:25,800
Speaker 4: Boon and Crockett and the Ethics Committee just did a

1393
01:19:25,800 --> 01:19:28,360
Speaker 4: fair chase module for part of the n RA, a

1394
01:19:28,479 --> 01:19:33,000
Speaker 4: Hunter ed. You can go on to their n RAS platform,

1395
01:19:33,040 --> 01:19:36,880
Speaker 4: their Hunter edueducation platform. There's a fair Chase module that's

1396
01:19:37,000 --> 01:19:39,040
Speaker 4: kind of a two to oh one level. You know,

1397
01:19:39,080 --> 01:19:43,559
Speaker 4: maybe you've got a you know, team to twenties, it's

1398
01:19:43,600 --> 01:19:45,800
Speaker 4: starting to get into this hunting, really starting to take it.

1399
01:19:45,840 --> 01:19:50,360
Speaker 4: We tried to dedicate that fair chase messaging to that demographic.

1400
01:19:51,479 --> 01:19:55,519
Speaker 4: Jim Posowitz has a book Beyond Fair Chase. The Pause

1401
01:19:55,640 --> 01:19:58,720
Speaker 4: was awesome. It's great. You know, you're probably not going

1402
01:19:58,760 --> 01:20:00,840
Speaker 4: to agree with everything he wrote, but he brings up

1403
01:20:00,880 --> 01:20:03,320
Speaker 4: some very good questions if you really want to question

1404
01:20:03,400 --> 01:20:05,880
Speaker 4: this idea of fair chase and what is and isn't okay?

1405
01:20:07,320 --> 01:20:09,400
Speaker 4: You know, like I said, it just depends on the

1406
01:20:09,479 --> 01:20:12,240
Speaker 4: level of engagement in both clubs websites. I mean, there's

1407
01:20:12,280 --> 01:20:15,160
Speaker 4: a there's a fair Chase essay that Boone and Crockett

1408
01:20:15,160 --> 01:20:17,519
Speaker 4: did that kind of looks at some situations. Pope and

1409
01:20:17,560 --> 01:20:21,920
Speaker 4: Young has position statements on sites and definition.

1410
01:20:21,479 --> 01:20:24,439
Speaker 3: Of a bow. There's there's a ton out there.

1411
01:20:25,240 --> 01:20:29,280
Speaker 5: Do you have any favorites, John, I'm going to take

1412
01:20:29,280 --> 01:20:32,280
Speaker 5: the low hanging flute fruit and say every hunter should

1413
01:20:32,280 --> 01:20:37,640
Speaker 5: read a Sad County Almanac and if you're only going

1414
01:20:37,720 --> 01:20:40,080
Speaker 5: to pick one thing within that book where you think

1415
01:20:40,160 --> 01:20:43,200
Speaker 5: like a mountain and I think that's a good way

1416
01:20:43,240 --> 01:20:48,440
Speaker 5: to stay grounded and see how far this wildlife conservation

1417
01:20:48,560 --> 01:20:53,280
Speaker 5: wildlife management movement has come. And I think that's good

1418
01:20:53,280 --> 01:20:55,519
Speaker 5: for everybody to take to the duck blind, to the

1419
01:20:55,520 --> 01:21:00,320
Speaker 5: deer stand read in the offseason. It's just it's keep

1420
01:21:00,360 --> 01:21:03,479
Speaker 5: us all looking in the right direction.

1421
01:21:05,400 --> 01:21:08,160
Speaker 2: Yeah, I couldn't. I couldn't agree more with you on

1422
01:21:08,160 --> 01:21:11,880
Speaker 2: that one. Justin I want to give you an opportunity

1423
01:21:11,880 --> 01:21:16,080
Speaker 2: for the last word, just to plug Pope and Young

1424
01:21:16,120 --> 01:21:20,120
Speaker 2: if you'd like, and give folks explicit directions on the

1425
01:21:20,200 --> 01:21:24,479
Speaker 2: U r L on any specific actions you'd like them

1426
01:21:24,520 --> 01:21:27,880
Speaker 2: to take. Uh. You know, something we maybe should have

1427
01:21:27,960 --> 01:21:32,000
Speaker 2: covered but didn't would even be a walkthrough of exactly

1428
01:21:32,040 --> 01:21:34,840
Speaker 2: what goes into making a submission to Pope and young.

1429
01:21:35,560 --> 01:21:38,080
Speaker 2: If you want to tackle any one of those, you're

1430
01:21:38,120 --> 01:21:39,120
Speaker 2: welcome to you, you know.

1431
01:21:39,120 --> 01:21:43,240
Speaker 4: I mean, go to the website Hope Have Hope hyphenyong

1432
01:21:43,320 --> 01:21:48,160
Speaker 4: dot org. There's there's membership options there. We'd love to

1433
01:21:48,160 --> 01:21:50,080
Speaker 4: have you as a member. We'd love to have an entry.

1434
01:21:50,960 --> 01:21:53,880
Speaker 4: There's a list of all the official measures. You know,

1435
01:21:53,920 --> 01:21:55,519
Speaker 4: if you're in an area that there's not a lot

1436
01:21:55,560 --> 01:21:57,639
Speaker 4: of official measures, we'd love to have you fill out

1437
01:21:57,640 --> 01:22:00,639
Speaker 4: one of those online applications and maybe get involve as

1438
01:22:00,680 --> 01:22:04,680
Speaker 4: a volunteer for US SCORING twenty twenty five, we're doing

1439
01:22:04,720 --> 01:22:08,599
Speaker 4: a big convention in Glendale, Arizona, April nine through twelve.

1440
01:22:08,600 --> 01:22:11,559
Speaker 4: We'd love to have you come out check out our convention.

1441
01:22:12,080 --> 01:22:14,360
Speaker 4: There'll be you know, seminars on these topics. You know,

1442
01:22:14,400 --> 01:22:16,160
Speaker 4: maybe maybe Mark would be nice enough to come out

1443
01:22:16,200 --> 01:22:17,800
Speaker 4: and talk to us out there. We'll see hit them

1444
01:22:17,880 --> 01:22:21,240
Speaker 4: up for that. But you know, anywhere you can get involved,

1445
01:22:21,240 --> 01:22:22,760
Speaker 4: we'd love to have you in the website. It's a

1446
01:22:22,760 --> 01:22:25,840
Speaker 4: great place to start, and uh, there's all kinds of

1447
01:22:25,840 --> 01:22:29,000
Speaker 4: different links depending on you know, conservation, whatever you want

1448
01:22:29,000 --> 01:22:31,920
Speaker 4: to go down. That's that's the best place to start.

1449
01:22:31,960 --> 01:22:33,960
Speaker 4: And we'd love love to have you in our ranks,

1450
01:22:34,000 --> 01:22:36,360
Speaker 4: and you know, we talk a lot about the future.

1451
01:22:36,360 --> 01:22:38,800
Speaker 4: Man get involved, help, you know, help me do my job,

1452
01:22:38,840 --> 01:22:41,240
Speaker 4: do one of the voices in the organization that rises

1453
01:22:41,320 --> 01:22:42,799
Speaker 4: up and helps direct our future.

1454
01:22:44,120 --> 01:22:47,960
Speaker 2: Love it all right, gentlemen, Justin John, thank you so

1455
01:22:48,080 --> 01:22:49,639
Speaker 2: much for taking the time to have this conversation.

1456
01:22:50,520 --> 01:22:50,960
Speaker 3: Thank you.

1457
01:22:53,680 --> 01:22:56,240
Speaker 2: All right, and that's a wrap. Thanks for being here.

1458
01:22:56,479 --> 01:22:58,960
Speaker 2: I appreciate you. Thank you for being a part of

1459
01:22:59,040 --> 01:23:03,599
Speaker 2: this community. Until next time, stay wired to hunt.

1460
01:23:11,080 --> 01:23:11,479
Speaker 4: Mm hmm.